Journal articles on the topic 'Autonomous vehicle safety measures'
To see the other types of publications on this topic, follow the link: Autonomous vehicle safety measures.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Autonomous vehicle safety measures.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Pauwels, Alex, Nadia Pourmohammad-Zia, and Frederik Schulte. "Safety and Sustainable Development of Automated Driving in Mixed-Traffic Urban Areas—Considering Vulnerable Road Users and Network Efficiency." Sustainability 14, no. 20 (October 19, 2022): 13486. http://dx.doi.org/10.3390/su142013486.
Full textAbstract:
Next to environmental aspects, establishing areas for safe and economically viable automated driving in mixed-traffic settings is one major challenge for sustainable development of Autonomous Vehicles (AVs). This work investigates safety in the interactions between AVs, human-driven vehicles, and vulnerable road users, including cyclists and pedestrians, within a simulated urban environment in the Dutch city of Rotterdam. New junction and pedestrian models are introduced, and virtual AVs with an occlusion-aware driving system are deployed to deliver cargo autonomously. The safety of applying this autonomous cargo delivery service is assessed using a large set of Surrogate Safety Indicators (SSIs). Furthermore, Macroscopic Fundamental Diagrams (MFDs) and travel time loss are incorporated to evaluate the network efficiency. By assessing the impact of various measures involving Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Everything (V2X) communications, infrastructure modifications, and driving behavior, we show that traffic safety and network efficiency can be achieved in a living lab setting for the considered case. Our findings further suggest that V2X gets implemented, new buildings are not placed close to intersections, and the speed limit of non-arterial roads is lowered.
2
Morando, Mark Mario, Qingyun Tian, Long T. Truong, and Hai L. Vu. "Studying the Safety Impact of Autonomous Vehicles Using Simulation-Based Surrogate Safety Measures." Journal of Advanced Transportation 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/6135183.
Full textAbstract:
Autonomous vehicle (AV) technology has advanced rapidly in recent years with some automated features already available in vehicles on the market. AVs are expected to reduce traffic crashes as the majority of crashes are related to driver errors, fatigue, alcohol, or drugs. However, very little research has been conducted to estimate the safety impact of AVs. This paper aims to investigate the safety impacts of AVs using a simulation-based surrogate safety measure approach. To this end, safety impacts are explored through the number of conflicts extracted from the VISSIM traffic microsimulator using the Surrogate Safety Assessment Model (SSAM). Behaviours of human-driven vehicles (HVs) and AVs (level 4 automation) are modelled within the VISSIM’s car-following model. The safety investigation is conducted for two case studies, that is, a signalised intersection and a roundabout, under various AV penetration rates. Results suggest that AVs improve safety significantly with high penetration rates, even when they travel with shorter headways to improve road capacity and reduce delay. For the signalised intersection, AVs reduce the number of conflicts by 20% to 65% with the AV penetration rates of between 50% and 100% (statistically significant at p<0.05). For the roundabout, the number of conflicts is reduced by 29% to 64% with the 100% AV penetration rate (statistically significant at p<0.05).
3
Bai, Jiarui. "Research on the Application of Deep Learning in Automatic Vehicle." SHS Web of Conferences 144 (2022): 03021. http://dx.doi.org/10.1051/shsconf/202214403021.
Full textAbstract:
In recent years, various kinds of applications of artificial intelligence technology to the automatic driving vehicle were widely reported by the media, also aroused public interest. According to a survey by the Ministry of Public Security, there are more than 500 million motor vehicles in China in 2021 and more than 200 thousand traffic accidents occurred in 2021, which is a great challenge to traffic safety protection. Human reaction time, which consists of perception time and judgment time, is too long to take measures. Besides, human drivers may find it difficult to even make the right response in some complex road conditions. To improve this situation, the Intelligent Transportation System (ITS) may transform the traditional passive safety that takes protective measures after accidents into active safety that focuses on prevention. Deep learning network receives information through the vehicle-loaded sensor then makes judgments by its computing unit and prompts drivers the possible danger. This article reviewed the performance of different deep learning models in autonomous driving vehicles.
4
Shin, Woo-Ri, Hyun-Bin Jo, Min-Sang Cho, and Ho-Dae Cho. "A Study on the Safety Management Measures of Autonomous vehicle in disaster situation." Korean Journal of Security Convergence Management 9, no. 1 (February 28, 2020): 133–42. http://dx.doi.org/10.24826/kscs.9.1.9.
Full text
5
Zheng, Binshuang, Xiaoming Huang, Runmin Zhao, Zhengqiang Hong, Jiaying Chen, and Shengze Zhu. "Study on the Rut Control Threshold of Asphalt Pavement Considering Steering Stability of Autonomous Vehicles Based on Fuzzy Control Theory." Advances in Civil Engineering 2021 (April 17, 2021): 1–13. http://dx.doi.org/10.1155/2021/8879900.
Full textAbstract:
To fully consider the impact of asphalt pavement rut on steering stability of autonomous vehicles, the sensitivity of various indicators of rut shape to vehicle stability was comprehensively measured, and pavement rut control standards based on comfort demands of autonomous vehicles were investigated. Firstly, a steering control system for autonomous vehicles was built in Simulink according to fuzzy control theory. Then, through orthogonal experiment design theory, different rut shape indicators are simulated in CarSim. The influence sensitivity of different rut shape indicators and the allowable rut range considering driving comfort were studied. The results show that both the rut depth and the rut side angle have a greater effect on the vehicle vertical acceleration within a certain parameter range. The maximum roll angle of vehicle body is mainly affected by the rut depth, and the rut width has a small effect on the vehicle driving stability. Meanwhile, considering human comfort, the rut side angle should not be greater than 1° when the rut depth reaches 2 cm. For autonomous driving, the rut depth should not exceed 2.5 cm. When the rut depth exceeds 2.5 cm, the vehicle body roll angle caused by the rut exceeds the inertial centrifugal force of the vehicle itself, which has a significant impact on the passenger comfort and safety.
6
Žuraulis, Vidas, Vytenis Surblys, and Eldar Šabanovič. "TECHNOLOGICAL MEASURES OF FOREFRONT ROAD IDENTIFICATION FOR VEHICLE COMFORT AND SAFETY IMPROVEMENT." Transport 34, no. 3 (May 27, 2019): 363–72. http://dx.doi.org/10.3846/transport.2019.10372.
Full textAbstract:
This paper presents the technological measures currently being developed at institutes and vehicle research centres dealing with forefront road identification. In this case, road identification corresponds with the surface irregularities and road surface type, which are evaluated by laser scanning and image analysis. Real-time adaptation, adaptation in advance and system external informing are stated as sequential generations of vehicle suspension and active braking systems where road identification is significantly important. Active and semi-active suspensions with their adaptation technologies for comfort and road holding characteristics are analysed. Also, an active braking system such as Anti-lock Braking System (ABS) and Autonomous Emergency Braking (AEB) have been considered as very sensitive to the road friction state. Artificial intelligence methods of deep learning have been presented as a promising image analysis method for classification of 12 different road surface types. Concluding the achieved benefit of road identification for traffic safety improvement is presented with reference to analysed research reports and assumptions made after the initial evaluation.
7
El-Hansali, Youssef, Siham Farrag, Ansar Yasar, Elhadi Shakshuki, and Khalid Al-Abri. "Using Surrogate Measures to Evaluate the Safety of Autonomous Vehicles." Procedia Computer Science 191 (2021): 151–59. http://dx.doi.org/10.1016/j.procs.2021.07.020.
Full text
8
Lahdya, S., and T. Mazri. "DATA SECURITY CHALLENGES IN SELF-DRIVING CAR." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W3-2022 (December 2, 2022): 61–66. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w3-2022-61-2022.
Full textAbstract:
Abstract. As modern vehicles are sophisticated IoT devices with intelligence, capable of connecting to external infrastructure and utilizing vehicle-to-everything (V2X) communications, communications need to be secured so that cyberattacks do not reach their destination. In addition, the various organs of the car (sensors, communications, and controls) can have weaknesses that lead to accidents or potential fatalities. Automakers have a major responsibility for the safety of their customers and should not ignore important security research, but should ensure that important security measures are implemented that are less likely to attack your vehicle. This paper addresses the relevant attacks and threats to modern vehicles and presents a security analysis and possible countermeasures. We discussed the future of modern and autonomous vehicles and concluded that further countermeasures must be taken to create a future-proof concept.
9
Giuffrè, Tullio, Anna Granà, and Salvatore Trubia. "Safety Evaluation of Turbo-Roundabouts with and without Internal Traffic Separations Considering Autonomous Vehicles Operation." Sustainability 13, no. 16 (August 6, 2021): 8810. http://dx.doi.org/10.3390/su13168810.
Full textAbstract:
The paper presents a microsimulation approach for assessing the safety performance of turbo-roundabouts where Cooperative Autonomous Vehicles “CAVs” have been introduced into the traffic mix alongside conventional vehicles “CVs”. Based on the analysis of vehicle trajectories from VISSIM and subsequent analysis of traffic conflicts through the Surrogate Safety Assessment Model (SSAM), the research aims to evaluate the safety benefits of turbo-roundabouts where the lanes are physically separated by raised curbs, compared to roundabouts without such curbs. The paper will then describe the methodological path followed to build VISSIM models of turbo-roundabouts with and without raised curbs in order to calibrate the simulation models and estimate the potential conflicts when a higher percentage of CAVs are introduced into the traffic mix. A criterion has been also proposed for setting properly the principal SSAM filters. The results confirmed both higher safety levels for turbo-roundabouts equipped with raised lane dividers compared to turbo-roundabout solutions without curbs, and better safety conditions under the traffic mix of CVs and CAVs. Therefore, it follows that, in absence of crash data including CAVs, the surrogate measures of safety are the only approach in which the safety performance of any roundabout or road entity can be evaluated.
10
Wang, Song, and Zhixia Li. "Roadside Sensing Information Enabled Horizontal Curve Crash Avoidance System Based on Connected and Autonomous Vehicle Technology." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 5 (March 22, 2019): 49–60. http://dx.doi.org/10.1177/0361198119837957.
Full textAbstract:
Horizontal curves are a major cause of road departure crashes that lead to fatal and severe injuries. Existing curve crash avoidance systems are typically enabled by displaying safety messages via in-vehicle heads-up/down display. However, these systems just pass the information to drivers. The final decision to reduce or maintain speed in response to the safety message is still the human driver’s. Because of the involvement of the human factor, there is potential for road departure crashes to happen if human drivers do not respond to safety messages appropriately. Autonomous vehicle (AV) technology targets elimination of human error in driving through an automated driving system. In this context, this paper proposes a conceptual prototype of a connected and AV-based horizontal curve crash avoidance system (CAV-HCCAS), aiming at achieving a permanent solution to horizontal curve safety by excluding human error through automated driving. In CAV-HCCAS, a roadside sensor detects pavement wetness level at the horizontal curve and communicates it to the vehicle via dedicated short-range communication. By processing the pavement wetness information, the AV applies a safe curve travel speed that reflects the real-time pavement conditions. An automated driving simulation experiment was performed to prove the concept. Dry and wet pavement conditions were simulated for a horizontal curve. Lane deviation data and the resulting lane departure conflicts were measured as safety performance measures. Results indicate a significant reduction of lane departure conflicts when CAV-HCCAS is implemented under both dry and wet pavement conditions, which reflects a substantial safety benefit.
11
Das, Anik, Md Nasim Khan, and Mohamed M. Ahmed. "Nonparametric Multivariate Adaptive Regression Splines Models for Investigating Lane-Changing Gap Acceptance Behavior Utilizing Strategic Highway Research Program 2 Naturalistic Driving Data." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 5 (April 10, 2020): 223–38. http://dx.doi.org/10.1177/0361198120914293.
Full textAbstract:
Gap acceptance is one of the crucial components of lane-changing analysis and an important parameter in microsimulation modeling. Drivers’ poor gap judgment, and failure to accept a necessary safety gap, make it one of the major causes of lane-changing crashes on roadways. Several studies have been conducted to investigate lane-changing gap acceptance behavior; however, very few studies examined the behavior in complex real-world situations, such as in naturalistic settings. This study examined lane-changing gap acceptance behavior from the big Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study (NDS) datasets using a nonparametric multivariate adaptive regression splines (MARS) approach to better understand the complex effects of different factors in gap acceptance behavior. The study developed a unique methodology to identify lane-changing events of the non-NDS-vehicles using the front-mounted radar data from NDS vehicles and extract necessary parameters for analyzing gap acceptance behavior. In addition, surrogate measures of safety, that is, time-to-collision (TTC), was utilized to understand the impact of lane-changing on the NDS following vehicle safety. Moreover, different distributions of gap acceptance were fitted to identify the trend of gap acceptance behavior. The results from the MARS model revealed that different factors including relative speed between lane-changing vehicle (LCV) and lead vehicle (LV)/following vehicle (FV), traffic conditions, acceleration of LCV and FV, and roadway geometric characteristics have significant effects on gap acceptance behavior. The results of this study have significant implications, which could be used in microsimulation model calibration and safety improvements in connected and autonomous vehicles (CAV).
12
Zlatkovic, Milan, and Andalib Shams. "Platoon Signal Priority in Connected-Autonomous Vehicle Environments: Algorithm Development and Testing." Put i saobraćaj 65, no. 4 (December 23, 2019): 1–9. http://dx.doi.org/10.31075/pis.65.04.01.
Full textAbstract:
As traffic congestion increases day by day, it becomes necessary to improve the existing roadway facilities to maintain satisfactory operational and safety performances. New vehicle technologies, such as Connected and Autonomous Vehicles (CAV) have a potential to significantly improve transportation systems. Using the advantages of CAVs, this study developed signalized intersection control strategy algorithm that optimizes the operations of CAVs and allows signal priority for connected platoons. The algorithm was tested in VISSIM microsimulation using a real-world urban corridor. The tested scenarios include a 2040 Do-Nothing scenario, and CAV alternatives with 25%, 50%, 75% and 100% CAV penetration rate. The results show a significant reduction in intersection delays (26% - 38%) and travel times (6% - 20%), depending on the penetration rate, as well as significant improvements on the network-wide level. CAV penetration rates of 50% or more have a potential to significantly improve all operational measures of effectiveness.
13
Zhang, Jian, Kunrun Wu, Min Cheng, Min Yang, Yang Cheng, and Shen Li. "Safety Evaluation for Connected and Autonomous Vehicles’ Exclusive Lanes considering Penetrate Ratios and Impact of Trucks Using Surrogate Safety Measures." Journal of Advanced Transportation 2020 (January 22, 2020): 1–16. http://dx.doi.org/10.1155/2020/5847814.
Full textAbstract:
Plenty of studies on exclusive lanes for Connected and Autonomous Vehicle (CAV) have been conducted recently about traffic efficiency and safety. However, most of the previous research studies neglected comprehensive consideration of the safety impact on different market penetration rates (MPRs) of CAVs, traffic demands, and proportion of trucks in mixture CAVs with human’s driven vehicle environment. On this basis, this study is to (1) identify the safety impact on exclusive lanes for CAVs under different MPRs with different traffic demands and (2) investigate the safety impact of trucks for CAV exclusive lanes on mixture environment. Based on the Intelligent Driver Model (IDM), a CAV platooning control algorithm is proposed for modeling the driving behaviors of CAVs. A calibrated 7-kilometer freeway section microscopic simulation environment is built by VISSIM. Four surrogate safety measures, including both longitudinal and lateral safety risk indexes, are employed to evaluate the overall safety impacts of setting exclusive lanes. Main results indicate that (1) setting one exclusive lane is capable to improve overall safety environment in low demand, and two exclusive lanes are more suitable for high-demand scenario; (2) existence of trucks worsens overall longitudinal safety environment, and improper setting of exclusive lanes in high trucks, low MPR scenario has adverse effect on longitudinal safety; and (3) setting exclusive lanes have better longitudinal and lateral safety improvement in high-truck proportion scenarios. Setting one or two exclusive lanes led to [+42.4% to −52.90%] and [+45.7% to −55.2%] of longitudinal risks while [−1.8% to −87.1%] and [−2.1% to −85.3%] of lateral conflicts compared with the base scenario, respectively. Results of this study provide useful insight for the setting of exclusive lanes for CAVs in a mixture environment.
14
Brocklehurst, Callum, and Milena Radenkovic. "Resistance to Cybersecurity Attacks in a Novel Network for Autonomous Vehicles." Journal of Sensor and Actuator Networks 11, no. 3 (July 13, 2022): 35. http://dx.doi.org/10.3390/jsan11030035.
Full textAbstract:
The increased interest in autonomous vehicles has led to the development of novel networking protocols in VANETs In such a widespread safety-critical application, security is paramount to the implementation of the networks. We view new autonomous vehicle edge networks as opportunistic networks that bridge the gap between fully distributed vehicular networks based on short-range vehicle-to-vehicle communication and cellular-based infrastructure for centralized solutions. Experiments are conducted using opportunistic networking protocols to provide data to autonomous trams and buses in a smart city. Attacking vehicles enter the city aiming to disrupt the network to cause harm to the general public. In the experiments the number of vehicles and the attack length is altered to investigate the impact on the network and vehicles. Considering different measures of success as well as computation expense, measurements are taken from all nodes in the network across different lengths of attack. The data gathered from each node allow exploration into how different attacks impact metrics including the delivery probability of a message, the time taken to deliver and the computation expense to each node. The novel multidimensional analysis including geospatial elements provides evidence that the state-of-the-art MaxProp algorithm outperforms the benchmark as well as other, more complex routing protocols in most of the categories. Upon the introduction of attacking nodes however, PRoPHET provides the most reliable delivery probability when under attack. Two different attack methods (black and grey holes) are used to disrupt the flow of messages throughout the network and the more basic protocols show that they are less consistent. In some metrics, the PRoPHET algorithm performs better when under attack due to the benefit of reduced network traffic.
15
Peralta, Federico, Mario Arzamendia, Derlis Gregor, Daniel G. Reina, and Sergio Toral. "A Comparison of Local Path Planning Techniques of Autonomous Surface Vehicles for Monitoring Applications: The Ypacarai Lake Case-study." Sensors 20, no. 5 (March 9, 2020): 1488. http://dx.doi.org/10.3390/s20051488.
Full textAbstract:
Local path planning is important in the development of autonomous vehicles since it allows a vehicle to adapt their movements to dynamic environments, for instance, when obstacles are detected. This work presents an evaluation of the performance of different local path planning techniques for an Autonomous Surface Vehicle, using a custom-made simulator based on the open-source Robotarium framework. The conducted simulations allow to verify, compare and visualize the solutions of the different techniques. The selected techniques for evaluation include A*, Potential Fields (PF), Rapidly-Exploring Random Trees* (RRT*) and variations of the Fast Marching Method (FMM), along with a proposed new method called Updating the Fast Marching Square method (uFMS). The evaluation proposed in this work includes ways to summarize time and safety measures for local path planning techniques. The results in a Lake environment present the advantages and disadvantages of using each technique. The proposed uFMS and A* have been shown to achieve interesting performance in terms of processing time, distance travelled and security levels. Furthermore, the proposed uFMS algorithm is capable of generating smoother routes.
16
Gadepally, Vijay, Ashok Krishnamurthy, and Ümit Özgüner. "A Framework for Estimating Long Term Driver Behavior." Journal of Advanced Transportation 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/3080859.
Full textAbstract:
We present a framework for estimation of long term driver behavior for autonomous vehicles and vehicle safety systems. The Hybrid State System and Hidden Markov Model (HSS+HMM) system discussed in this article is capable of describing the hybrid characteristics of driver and vehicle coupling. In our model, driving observations follow a continuous trajectory that can be measured to create continuous state estimates. These continuous state estimates can then be used to estimate the most likely driver state using decision-behavior coupling inherent to the HSS+HMM system. The HSS+HMM system is encompassed in a HSS structure and intersystem connectivity is determined by using signal processing and pattern recognition techniques. The proposed method is suitable for a number of autonomous and vehicle safety scenarios such as estimating intent of other vehicles near intersections or avoiding hazardous driving events such as unexpected lane changes. The long term driver behavior estimation system involves an extended HSS+HMM structure that is capable of including external information in the estimation process. Through the grafting and pruning of metastates, the HSS+HMM system can be dynamically updated to best represent driver choices given external information. Three application examples are also provided to elucidate the theoretical system.
17
Tang, Tianpei, Xiwei Wang, Jianbing Wu, Meining Yuan, Yuntao Guo, and Xunqian Xu. "Determinants and the Moderating Effects of Individual Characteristics on Autonomous Vehicle Adoption in China." International Journal of Environmental Research and Public Health 20, no. 1 (December 20, 2022): 43. http://dx.doi.org/10.3390/ijerph20010043.
Full textAbstract:
Along with the increasing popularity of autonomous vehicles (AVs), urban livability and public health will be enhanced due to ecofriendly issues: alleviated traffic congestion, lower car ownership, and reduced transport emissions. However, some emerging issues, including public safety, trust, privacy, reliability, underdeveloped legislation, and liability, may deter user intentions to adopt an AV. This study introduces an extensive factor, playfulness, into the technology acceptance model (TAM) to quantify the impacts of psychological factors (perceived usefulness, perceived ease of use, and perceived playfulness) on AV adoption intention. This study proposes four AV-related policy measures (financial incentivization, information dissemination, convenience, and legal normalization) and examines how policy measures motivate users to adopt an AV to facilitate public safety. Furthermore, this study investigated the moderating effects of demographic factors on the relationships between independent variables and AV adoption intention. Two models were proposed and estimated using a total of 1831 survey responses in China. The psychology-related and policy-related models explained 62.2% and 33.6% of the variance in AV adoption intention, respectively. The results suggest that perceived playfulness (β = 0.524, p < 0.001) and information dissemination (β = 0.348, p < 0.001) are the most important influencing factors of AV adoption intention. In addition, demographic factors (gender, education, income, the number of private cars owned by a family, and types of cities) can moderate the effects of psychological factors and policy measures on user intentions to adopt an AV. These insights can be employed to design more cost-effective policies and strategies for subgroups of the population to maximize the AV adoption intention.
18
Das, Tanmay, M. Shoaib Samandar, and Nagui Rouphail. "Longitudinal traffic conflict analysis of autonomous and traditional vehicle platoons in field tests via surrogate safety measures." Accident Analysis & Prevention 177 (November 2022): 106822. http://dx.doi.org/10.1016/j.aap.2022.106822.
Full text
19
Zhang, Hui, Ninghao Hou, Jianhua Zhang, Xuyi Li, and Yan Huang. "Evaluating the Safety Impact of Connected and Autonomous Vehicles with Lane Management on Freeway Crash Hotspots Using the Surrogate Safety Assessment Model." Journal of Advanced Transportation 2021 (July 6, 2021): 1–14. http://dx.doi.org/10.1155/2021/5565343.
Full textAbstract:
One goal for large-scale deployment of connected and autonomous vehicles is to achieve the traffic safety benefit since connected and autonomous vehicles (CAVs) could reduce the collision risk by enhancing the driver’s situation perception ability. Previous studies have analyzed the safety impact of CAVs involved in traffic, but only few studies examined the safety benefits brought by CAVs when approaching high-collision-risk road segments such as the freeway crash hotspots. This study chooses one freeway crash hotspot in Wuhan, China, as an instance and attempts to estimate the safety benefits for differential penetration rates (PRs) of CAVs using the surrogate safety assessment model (SSAM). First, the freeway crash hotspot is identified with kernel density estimation and simulated by VISSIM. Then, the intelligent driver model (IDM) and Wiedemann 99 (a car-following model) are adopted and calibrated to control the driving behaviors of CAVs and human-driven vehicles (HVs) in this study, respectively. The impact that rather CAVs are constrained with or without managed lanes on traffic safety is also discussed, and the PR of CAVs is set from 10% to 90%. The results of this study show that when the PR of CAVs is lower than 50%, there is no significant improvement on the safety measures such as conflicts, acceleration, and velocity difference, which are extracted from the vehicle trajectory data using SSAM. When the penetration rate is over 70%, the experiment results demonstrate that the traffic flow passing the freeway hotspot is with fewer conflicts, smaller acceleration, and smaller velocity difference in the scenario where CAVs are constrained with managed lane compared with the scenario without managed lane control. The safety benefit that CAVs bring needs to be discussed. The lane management of CAVs will also lead to distinct safety impact.
20
Essa, Mohamed, and Tarek Sayed. "Comparison between Surrogate Safety Assessment Model and Real-Time Safety Models in Predicting Field-Measured Conflicts at Signalized Intersections." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 3 (February 27, 2020): 100–112. http://dx.doi.org/10.1177/0361198120907874.
Full textAbstract:
Traffic simulation models are frequently used to evaluate the safety of signalized intersections, especially when testing unconventional designs or investigating the effects of emerging technologies such as connected and autonomous vehicles. In this approach, vehicle trajectories extracted from traffic simulation are usually analyzed using the surrogate safety assessment model (SSAM) to estimate the number and severity of traffic conflicts. However, recent research has shown that evaluating safety using SSAM has several limitations. First, a rigorous calibration procedure must be applied to the simulation model to obtain reliable conflict results. Second, simulation models in many cases do not accurately represent actual driving behavior. Subsequently, they often fail to capture the actual mechanisms generating near-misses. This paper presents a new procedure, alternative to SSAM, for evaluating the safety of signalized intersections. The procedure combines simulated vehicle trajectories with real-time safety models to predict rear-end conflicts. The conflict prediction is based on dynamic traffic parameters, such as traffic volume and shock wave characteristics, repeatedly measured over a short time interval (a few seconds). To validate the proposed procedure, its performance was investigated in predicting traffic conflicts extracted from 54 hours of real-world video data at two signalized intersections in the city of Surrey, British Columbia. The predicted conflict results were compared with SSAM. Overall, the results showed that the proposed procedure outperforms SSAM in relation to accuracy of conflict prediction. Lastly, a case study of using the proposed procedure in evaluating the safety impact of a recently developed connected-vehicles application is presented.
21
Meyer, Sunniva F., Rune Elvik, and Espen Johnsson. "Risk analysis for forecasting cyberattacks against connected and autonomous vehicles." Journal of Transportation Security 14, no. 3-4 (November 12, 2021): 227–47. http://dx.doi.org/10.1007/s12198-021-00236-4.
Full textAbstract:
AbstractA security risk analysis was conducted to identify possible cyberattacks against a future transport system consisting of autonomous and connected vehicles. Six scenarios were developed: joyriding, kidnapping, domestic abuse, autopilot manipulation, a large transport accident, and paralysis of the transport system. Even if it were possible to increase the difficulty of conducting such cyberattacks, it might be impossible to eliminate such attacks entirely. Measures that limit the consequences will therefore be necessary. Such measures include safety measures in vehicles to protect their occupants in traffic accidents and measures that make vehicles easier to remove in case they do not function.
22
Wen, Xiamei, Liping Fu, Ting Fu, Jessica Keung, and Ming Zhong. "Driver Behavior Classification at Stop-Controlled Intersections Using Video-Based Trajectory Data." Sustainability 13, no. 3 (January 29, 2021): 1404. http://dx.doi.org/10.3390/su13031404.
Full textAbstract:
Understanding how drivers behave at stop-controlled intersection is of critical importance for the control and management of an urban traffic system. It is also a critical element of consideration in the burgeoning field of smart infrastructure and connected and autonomous vehicles (CAV). A number of past efforts have been devoted to investigating the driver behavioral patterns when they pass through stop-controlled intersections. However, the majority of these studies have been limited to qualitative descriptions and analyses of driver behavior due to the unavailability of high-resolution vehicle data and sound methodology for classifying various driver behaviors. In this paper, we introduce a methodology that uses computer-vision vehicle trajectory data and unsupervised clustering techniques to classify different types of driver behaviors, infer the underlying mechanism and compare their impacts on safety. Two major types of behaviors are investigated, including vehicle stopping behavior and vehicle approaching patterns, using two clustering algorithms: a bisecting K-means algorithm for classifying stopping behavior, and the improved density-based spatial clustering of applications with noise (DBSCAN) algorithm for classifying vehicle approaching patterns. The methodology is demonstrated using a case study involving five stop-controlled intersections in Montreal, Canada. The results from the analysis show that there exist five distinctive classes of driver behaviors representing different levels of risk in both vehicle stopping and approaching processes. This finding suggests that the proposed methodology could be applied to develop new safety surrogate measures and risk analysis methods for network screening and countermeasure analyses of stop-controlled intersections.
23
Yin, Shutong, Chunlin Yang, Ibna Kawsar, Haifeng Du, and Yongjun Pan. "Longitudinal Predictive Control for Vehicle-Following Collision Avoidance in Autonomous Driving Considering Distance and Acceleration Compensation." Sensors 22, no. 19 (September 28, 2022): 7395. http://dx.doi.org/10.3390/s22197395.
Full textAbstract:
In response to the widespread adoption of vehicle-following systems in autonomous applications, the demand for collision warning to enable safer functionalities is increasing. This study provides an approach for automated vehicle guidance to follow the preceding vehicles longitudinally and puts emphasis on the performance of collision avoidance. The safety distance model is established, which contains a distance compensation algorithm to deal with the special case on curved roads. By introducing the algorithm of velocity and distance prediction, the collision risks are detected and measured in real time. The objective function is established based on optimal control theory to solve the desired following acceleration. The control system designed with the method of proportion integration differentiation combines throttle percentage and brake pressure as outputs to compensate acceleration. In the Carsim and Simulink co-simulation platform, the control system for longitudinal collision avoidance is simulated and analysed for four typical working conditions: the preceding vehicle drives at a constant speed on straight and curved roads, while the preceding vehicle drives at various speeds on straight and curved roads. The results validate the feasibility and effectiveness of the proposed method, which can be used for the longitudinal control of vehicle-following active collision avoidance.
24
Ezawa, Kazuhiro, Pongsathorn Raksincharoensak, and Masao Nagai. "Hazard Anticipatory Autonomous Braking Control System Based on 2-D Pedestrian Motion Prediction." Journal of Robotics and Mechatronics 27, no. 6 (December 18, 2015): 636–44. http://dx.doi.org/10.20965/jrm.2015.p0636.
Full textAbstract:
<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/05.jpg"" width=""300"" /> The focused scenario</div>This paper discusses 2-dimensional (2-D) pedestrian motion prediction and autonomous braking control for enhancing the collision avoidance performance of an active safety system. The paper targets a typical scenario involving a pedestrian walking toward a parked vehicle on a crowded urban road. The pedestrian is not expected to continue walking in a straight line. Conventional first-order motion prediction accuracy alone is not enough to predict the pedestrian motion because prediction is based on the pedestrian’s current position and velocity within a finite time. We formulated a 2-D pedestrian motion model of the parked vehicle based on learning the measured trajectory of pedestrians in the same scenario. We then designed an autonomous braking control system based on whether the vehicle will overtake a pedestrian. We evaluated the validity of the proposed autonomous braking control system in simulation experiments.
25
Huang, Gaojian, Clayton Steele, Xinrui Zhang, and Brandon J. Pitts. "Multimodal Cue Combinations: A Possible Approach to Designing In-Vehicle Takeover Requests for Semi-autonomous Driving." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 1739–43. http://dx.doi.org/10.1177/1071181319631053.
Full textAbstract:
The rapid growth of autonomous vehicles is expected to improve roadway safety. However, certain levels of vehicle automation will still require drivers to ‘takeover’ during abnormal situations, which may lead to breakdowns in driver-vehicle interactions. To date, there is no agreement on how to best support drivers in accomplishing a takeover task. Therefore, the goal of this study was to investigate the effectiveness of multimodal alerts as a feasible approach. In particular, we examined the effects of uni-, bi-, and trimodal combinations of visual, auditory, and tactile cues on response times to takeover alerts. Sixteen participants were asked to detect 7 multimodal signals (i.e., visual, auditory, tactile, visual-auditory, visual-tactile, auditory-tactile, and visual-auditory-tactile) while driving under two conditions: with SAE Level 3 automation only or with SAE Level 3 automation in addition to performing a road sign detection task. Performance on the signal and road sign detection tasks, pupil size, and perceived workload were measured. Findings indicate that trimodal combinations result in the shortest response time. Also, response times were longer and perceived workload was higher when participants were engaged in a secondary task. Findings may contribute to the development of theory regarding the design of takeover request alert systems within (semi) autonomous vehicles.
26
Стрижак, А. О. "ORGANIZATION OF ACTIVITIES OF THE PATROL POLICE OF GEORGIA IN THE SPHERE OF ENSURING ROAD SAFETY." Juridical science 2, no. 4(106) (April 3, 2020): 158–69. http://dx.doi.org/10.32844/2222-5374-2020-106-4-2.20.
Full textAbstract:
The article analyzes the organizational structure and powers of the patrol police of Georgia in the field of road safety. It has been determined that the main body for managing the activities of the patrol police of Georgia is the Patrol Police Department of the Ministry of Internal Affairs, whose tasks are defined as: ensuring public safety and law and order, preventing and combating offenses; protection of individuals and legal entities from illegal encroachment on their rights, freedoms and legitimate interests; ensuring the safety of road users, monitoring compliance with traffic rules, taking measures to prevent road accidents. Based on the analysis of the provisions of the Law of Georgia «On Police», the Code of Administrative Offenses of Georgia and the Instruction on the Rules of Patrolling, the Patrol Police Service of the Ministry of Internal Affairs of Georgia determined that the patrol police of Georgia in the field of road traffic safety has the authority to regulate traffic, apply police measures, stop vehicle, removal of drivers from driving a vehicle, consideration of cases of administrative offenses and the imposition of administrative sanctions. It was revealed that the patrol police of Georgia is cooperating with the Ministry of Transport and Infrastructure on improving the road network and the Service Agency of the Ministry of Internal Affairs in the provision of police and administrative services to citizens. The main directions of the implementation of the experience of the patrol police of Georgia into national practice have been determined, in particular, in terms of the adoption of certain normative acts on the organization of patrolling (the so-called Charter of the patrol police), amending the existing normative acts on expanding the rights of patrol police officers to conduct measures to identify a person, initiate the adoption of certain instructions, orders, provisions on the automatic recording of traffic violations, amending the Law of Ukraine "On the National Police", granting powers to the patrol police of Ukraine in the field of creating a safe road network, as well as countering violations of the rules of parking, stopping and parking vehicles, creating an autonomous Patrol Police Service Center and ensuring its interaction with the centers for the provision of administrative services.
27
Cassese, Giovanni, Cosimo Lucci, Giovanni Savino, and Niccolò Baldanzini. "Analysis of the rider’s body movement during the intervention of the Autonomous Emergency Braking system for Motorcycles (MAEB)." IOP Conference Series: Materials Science and Engineering 1214, no. 1 (January 1, 2022): 012047. http://dx.doi.org/10.1088/1757-899x/1214/1/012047.
Full textAbstract:
Abstract Among the rider assistance systems for powered-two-wheelers (PTWs) that are currently in the developing stage, autonomous emergency braking (identified by the acronym MAEB - Motorcycle Autonomous Emergency Braking) was shown to be promising to significantly improve the safety of such vehicles. This system, which is already available on passenger cars and trucks (known as AEB), reduces the vehicle speed in the event of a forthcoming collision. The lack of implementation of AEB on standard motorcycles is due to the characteristic capsize instability of PTWs and their complex dynamics, which is, strongly influenced by the motion of the rider. In a recent field-test campaign within the EU funded project “PIONEERS”, tests were conducted with common riders as participants to evaluate the intervention of MAEB in urban riding scenarios. A combined analysis of the data recorded from the vehicle, data related to the movement of the rider’s body measured through an inertial measurement unit and videos recorded during the test, allowed characterizing the different behaviours of the rider’s body in response to the activation of the automatic braking system in straight riding conditions. The results showed that body movement can be used as an indicator of the riders’ ability to control the vehicle under automatic braking conditions. In addition, in tests conducted with 0.5 g automatic decelerations, riders showed to be able to recover to natural riding position within the timeframe of the automatic braking activation event. This study defined an innovative method for evaluating the response of motorcyclists to the braking intervention and provides insights into the applicability of MAEB on standard vehicles.
28
Napolitano Dell’Annunziata, Guido, Vincenzo Maria Arricale, Flavio Farroni, Andrea Genovese, Nicola Pasquino, and Giuseppe Tranquillo. "Estimation of Vehicle Longitudinal Velocity with Artificial Neural Network." Sensors 22, no. 23 (December 6, 2022): 9516. http://dx.doi.org/10.3390/s22239516.
Full textAbstract:
Vehicle dynamics control systems have a fundamental role in smart and autonomous mobility, where one of the most crucial aspects is the vehicle body velocity estimation. In this paper, the problem of a correct evaluation of the vehicle longitudinal velocity for dynamic control applications is approached using a neural networks technique employing a set of measured samples referring to signals usually available on-board, such as longitudinal and lateral acceleration, steering angle, yaw rate and linear wheel speed. Experiments were run on four professional driving circuits with very different characteristics, and the vehicle longitudinal velocity was estimated with different neural network training policies and validated through comparison with the measurements of the one acquired at the vehicle’s center of gravity, provided by an optical Correvit sensor, which serves as the reference (and, therefore, exact) velocity values. The results obtained with the proposed methodology are in good agreement with the reference values in almost all tested conditions, covering both the linear and the nonlinear behavior of the car, proving that artificial neural networks can be efficiently employed onboard, thereby enriching the standard set of control and safety-related electronics.
29
Zarkeshev, Azamat, and Csaba Csiszár. "Are People Ready to Entrust Their Safety to an Autonomous Ambulance as an Alternative and More Sustainable Transportation Mode?" Sustainability 11, no. 20 (October 11, 2019): 5595. http://dx.doi.org/10.3390/su11205595.
Full textAbstract:
Progress in advanced vehicular technologies and computer science leads to driverless vehicles becoming a reality. The standard ambulances might be replaced by driverless ambulances operated by an autopilot that provides passengers with a secure regime. Therefore, the study’s aim was to collate two different countries regarding the consumers’ preparedness to travel on such autonomous ambulances. To obtain a more demonstrative result, a questionnaire survey was conducted in two countries (Hungary and Kazakhstan). It also gathered ‘affect’ measures in order to figure out if ‘affect’ served as a medium between the ambulance mode and readiness to ride, and to understand whether emotions play any role during the decision making. The necessary data were collected through the so-called Autonomous Ambulance Acceptance Questionnaire. After conducting ‘t-Test’, ‘ANOVA’, and Mediation analysis, the findings have been discussed. The results help to realize the necessity of developing educational guides to prepare potential patients for Autonomous Vehicles technology.
30
Rouchitsas, Alexandros, and Håkan Alm. "Ghost on the Windshield: Employing a Virtual Human Character to Communicate Pedestrian Acknowledgement and Vehicle Intention." Information 13, no. 9 (September 7, 2022): 420. http://dx.doi.org/10.3390/info13090420.
Full textAbstract:
Pedestrians base their street-crossing decisions on vehicle-centric as well as driver-centric cues. In the future, however, drivers of autonomous vehicles will be preoccupied with non-driving related activities and will thus be unable to provide pedestrians with relevant communicative cues. External human–machine interfaces (eHMIs) hold promise for filling the expected communication gap by providing information about a vehicle’s situational awareness and intention. In this paper, we present an eHMI concept that employs a virtual human character (VHC) to communicate pedestrian acknowledgement and vehicle intention (non-yielding; cruising; yielding). Pedestrian acknowledgement is communicated via gaze direction while vehicle intention is communicated via facial expression. The effectiveness of the proposed anthropomorphic eHMI concept was evaluated in the context of a monitor-based laboratory experiment where the participants performed a crossing intention task (self-paced, two-alternative forced choice) and their accuracy in making appropriate street-crossing decisions was measured. In each trial, they were first presented with a 3D animated sequence of a VHC (male; female) that either looked directly at them or clearly to their right while producing either an emotional (smile; angry expression; surprised expression), a conversational (nod; head shake), or a neutral (neutral expression; cheek puff) facial expression. Then, the participants were asked to imagine they were pedestrians intending to cross a one-way street at a random uncontrolled location when they saw an autonomous vehicle equipped with the eHMI approaching from the right and indicate via mouse click whether they would cross the street in front of the oncoming vehicle or not. An implementation of the proposed concept where non-yielding intention is communicated via the VHC producing either an angry expression, a surprised expression, or a head shake; cruising intention is communicated via the VHC puffing its cheeks; and yielding intention is communicated via the VHC nodding, was shown to be highly effective in ensuring the safety of a single pedestrian or even two co-located pedestrians without compromising traffic flow in either case. The implications for the development of intuitive, culture-transcending eHMIs that can support multiple pedestrians in parallel are discussed.
31
Ahn, Dae-ryong, Seong-geun Shin, Yun-soek Baek, Hyuck-kee Lee, Ki-hong Park, and In-seong Choi. "A Study on Simulation Based Fault Injection Test Scenario and Safety Measure Time of Autonomous Vehicle Using STPA." Journal of The Korea Institute of Intelligent Transport Systems 18, no. 2 (April 30, 2019): 129–43. http://dx.doi.org/10.12815/kits.2019.18.2.129.
Full text
32
Sanchez, L. F., H. Abaunza, and P. Castillo. "User–Robot Interaction for Safe Navigation of a Quadrotor." Robotica 38, no. 12 (January 29, 2020): 2189–203. http://dx.doi.org/10.1017/s026357472000003x.
Full textAbstract:
SUMMARYThis paper introduces an intuitive and safe command approach for a quadrotor, where inertial and muscular gestures are used for semi-autonomous flight. A bracelet composed of gyroscopes, accelerometers, and electromyographic sensors is used to detect user gestures, then an algorithm is proposed to interpret the signals as flight commands. The main goal is to provide a wearable, easy-to-handle human–machine interface for users a to safely command this kind of vehicles, even for inexpert operators. Safety measures are incorporated in the scheme to further enhance the user’s experience. Experimental tests are performed to validate the proposal.
33
Wei, Xiangyu, Shixiang Tian, Zhangyin Dai, and Peng Li. "Statistical Analysis of Major and Extra Serious Traffic Accidents on Chinese Expressways from 2011 to 2021." Sustainability 14, no. 23 (November 27, 2022): 15776. http://dx.doi.org/10.3390/su142315776.
Full textAbstract:
In order to explore the law of major and extra serious traffic accidents on expressways in China, a total of 802 cases of major and extra serious traffic accidents on expressways in the past 10 years were collected, and statistical analysis was conducted from the aspects of time distribution, spatial distribution, accident form, and accident cause. The results show that in the past 10 years, the incidence and casualties of major and extra-serious traffic accidents on expressways have shown a fluctuating downward trend. In January, May, August, and November every year, between 6: 00 and 8: 00 every day is the highest incidence of accidents. The Guangxi Zhuang Autonomous Region, Heilongjiang Province, Fujian Province, and Anhui Province have more accidents. Vehicle collisions have the highest number of deaths, and rollover injuries have the highest rate. Human factors accounted for 72.1% of the causes of accidents, among which improper measures and speeding accounted for the largest proportion. Finally, according to the results of data statistical analysis, the corresponding control measures should be put forward in order to provide reference and technical support for the current highway traffic safety situation and safety management policy.
34
Cao, Shi, Pinyan Tang, and Xu Sun. "Driver Take-Over Reaction in Autonomous Vehicles with Rotatable Seats." Safety 6, no. 3 (July 1, 2020): 34. http://dx.doi.org/10.3390/safety6030034.
Full textAbstract:
A new concept in the interior design of autonomous vehicles is rotatable or swivelling seats that allow people sitting in the front row to rotate their seats and face backwards. In the current study, we used a take-over request task conducted in a fixed-based driving simulator to compare two conditions, driver front-facing and rear-facing. Thirty-six adult drivers participated in the experiment using a within-subject design with take-over time budget varied. Take-over reaction time, remaining action time, crash, situation awareness and trust in automation were measured. Repeated measures ANOVA and Generalized Linear Mixed Model were conducted to analyze the results. The results showed that the rear-facing configuration led to longer take-over reaction time (on average 1.56 s longer than front-facing, p < 0.001), but it caused drivers to intervene faster after they turned back their seat in comparison to the traditional front-facing configuration. Situation awareness in both front-facing and rear-facing autonomous driving conditions were significantly lower (p < 0.001) than the manual driving condition, but there was no significant difference between the two autonomous driving conditions (p = 1.000). There was no significant difference of automation trust between front-facing and rear-facing conditions (p = 0.166). The current study showed that in a fixed-based simulator representing a conditionally autonomous car, when using the rear-facing driver seat configuration (where participants rotated the seat by themselves), participants had longer take-over reaction time overall due to physical turning, but they intervened faster after they turned back their seat for take-over response in comparison to the traditional front-facing seat configuration. This behavioral change might be at the cost of reduced take-over response quality. Crash rate was not significantly different in the current laboratory study (overall the average rate of crash was 11%). A limitation of the current study is that the driving simulator does not support other measures of take-over request (TOR) quality such as minimal time to collision and maximum magnitude of acceleration. Based on the current study, future studies are needed to further examine the effect of rotatable seat configurations with more detailed analysis of both TOR speed and quality measures as well as in real world driving conditions for better understanding of their safety implications.
35
Mahlberg, Justin A., Rahul Suryakant Sakhare, Howell Li, Jijo K. Mathew, Darcy M. Bullock, and Gopi C. Surnilla. "Prioritizing Roadway Pavement Marking Maintenance Using Lane Keep Assist Sensor Data." Sensors 21, no. 18 (September 8, 2021): 6014. http://dx.doi.org/10.3390/s21186014.
Full textAbstract:
There are over four million miles of roads in the United States, and the prioritization of locations to perform maintenance activities typically relies on human inspection or semi-automated dedicated vehicles. Pavement markings are used to delineate the boundaries of the lane the vehicle is driving within. These markings are also used by original equipment manufacturers (OEM) for implementing advanced safety features such as lane keep assist (LKA) and eventually autonomous operation. However, pavement markings deteriorate over time due to the fact of weather and wear from tires and snowplow operations. Furthermore, their performance varies depending upon lighting (day/night) as well as surface conditions (wet/dry). This paper presents a case study in Indiana where over 5000 miles of interstate were driven and LKA was used to classify pavement markings. Longitudinal comparisons between 2020 and 2021 showed that the percentage of lanes with both lines detected increased from 80.2% to 92.3%. This information can be used for various applications such as developing or updating standards for pavement marking materials (infrastructure), quantifying performance measures that can be used by automotive OEMs to warn drivers of potential problems with identifying pavement markings, and prioritizing agency pavement marking maintenance activities.
36
Miller, Erika E., and Linda Ng Boyle. "Driver adaptation to lane keeping assistance systems: Do drivers become less vigilant?" Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 1934–38. http://dx.doi.org/10.1177/1541931213601963.
Full textAbstract:
Driver adaptation to semi-autonomous vehicles is examined in this study using a longitudinal driving simulator study with 18 subjects randomly assigned to a control (n = 9) and treatment (n = 9) group. A Tactile Detection Response Task (TDRT) was used to measure cognitive workload while drivers engaged in visual-manual distracting tasks using an in-vehicle information system, with and without a lane-keeping assistance system (baseline, intervention, and withdrawal period). Measures of miss rate and reaction time to the TDRT stimuli suggest that drivers in both groups showed decreases in cognitive workload as they gained experience. Participants in the treatment group experienced higher cognitive workload when the lane keeping assistance system was withdrawn. On average, there were greater decreases in cognitive workload over time for the difficult tasks as compared to the easy tasks for both the control and treatment groups. The effect was more pronounced in the control group with larger decreases in cognitive workload. The NASA TLX was also comparable to the task accuracy. These results suggest that adaptations in driver behavior due to exposure to automation may have transfer effects on cognitive workload and thus may lead to safety implications.
37
García-Castellano, González-Romo, Gómez-Galán, García-Martín, Torralba, and Pérez-Mira. "ITERL: A Wireless Adaptive System for Efficient Road Lighting." Sensors 19, no. 23 (November 21, 2019): 5101. http://dx.doi.org/10.3390/s19235101.
Full textAbstract:
This work presents the development and construction of an adaptive street lighting system that improves safety at intersections, which is the result of applying low-power Internet of Things (IoT) techniques to intelligent transportation systems. A set of wireless sensor nodes using the Institute of Electrical and Electronics Engineers (IEEE) 802.15.4 standard with additional internet protocol (IP) connectivity measures both ambient conditions and vehicle transit. These measurements are sent to a coordinator node that collects and passes them to a local controller, which then makes decisions leading to the streetlight being turned on and its illumination level controlled. Streetlights are autonomous, powered by photovoltaic energy, and wirelessly connected, achieving a high degree of energy efficiency. Relevant data are also sent to the highway conservation center, allowing it to maintain up-to-date information for the system, enabling preventive maintenance.
38
Ali Alheeti, Khattab M., Muzhir Shaban Al-Ani, Abdulkareem Kareem Najem Al-Aloosy, Abdulkareem Alzahrani, and Duaa Abdul Sattar Rukan. "Intelligent mobile detection of cracks in concrete utilising an unmanned aerial vehicle." Bulletin of Electrical Engineering and Informatics 11, no. 1 (February 1, 2022): 176–84. http://dx.doi.org/10.11591/eei.v11i1.2987.
Full textAbstract:
Cracks in constructions may result in negative consequences in terms of expenditure and safety. This in turn highlights the importance of finding ways to detect these cracks easily and effectively. Hence, technological advances play significant role in enabling effective and innovative ideas, such as the use of autonomous drones and artificial intelligence solutions. In this research, we utilise a type of drone called an unmanned aerial vehicle, equipped with a high-speed camera that can capture images of cracks in buildings, and pass the information to the system. We utilised a dataset that has images collected from different Middle East Technical University (METU) campus buildings with various concrete surfaces (with and without cracks). The crack detection approach uses statistical measures and a support vector machine that prevents overfitting, attains a good rate of accuracy, tackles problems in real-time, and can train a model when a small dataset exists. The combination of an unmanned aerial vehicle, artificial intelligence, and digital image processing gives excellent results. Performance metrics reported for seven rounds of experiments showed rates of accuracy in detection ranging from 83.3–100% (with 100% achieved in two rounds). This demonstrates the effectiveness of our proposed detection system in detecting cracks in constructions.
39
Hu, Juqi, Subhash Rakheja, and Youmin Zhang. "Tire–road friction coefficient estimation based on designed braking pressure pulse." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 235, no. 7 (January 5, 2021): 1876–91. http://dx.doi.org/10.1177/0954407020983580.
Full textAbstract:
Knowledge of tire–road friction coefficient (TRFC) is valuable for autonomous vehicle control and design of active safety systems. This paper investigates TRFC estimation on the basis of longitudinal vehicle dynamics. A two-stage TRFC estimation scheme is proposed that limits the disturbances to the vehicle motion. A sequence of braking pressure pulses is designed in the first stage to identify desired minimal pulse pressure for reliable estimation of TRFC with minimal interference with the vehicle motion. This stage also provides a qualitative estimate of TRFC. In the second stage, tire normal force and slip ratio are directly calculated from the measured signals, a modified force observer based on the wheel rotational dynamics is developed for estimating the tire braking force. A constrained unscented Kalman filter (CUKF) algorithm is subsequently proposed to identify the TRFC for achieving rapid convergence and enhanced estimation accuracy. The effectiveness of the proposed methodology is evaluated through CarSim™-MATLAB/Simulink™ co-simulations considering vehicle motions on high-, medium-, and low-friction roads at different speeds. The results suggest that the proposed two-stage methodology can yield an accurate estimation of the road friction with a relatively lower effect on the vehicle speed.
40
Israr, Ambar, Ghulam E. Mustafa Abro, M. Sadiq Ali Khan, Muhammad Farhan, and Saif ul Azrin Bin Mohd Zulkifli. "Internet of Things (IoT)-Enabled Unmanned Aerial Vehicles for the Inspection of Construction Sites: A Vision and Future Directions." Mathematical Problems in Engineering 2021 (April 28, 2021): 1–15. http://dx.doi.org/10.1155/2021/9931112.
Full textAbstract:
Today people are witnessing the rapid evolvement in every area. This is because of the emerging trends in communication technology and autonomous unmanned vehicles. These trends have led us towards the high standards of health, energy, transportation, monitoring, and surveillance of huge domestic and industrial projects. Thus, this review paper presents the integration of the latest trend in communication technology, i.e., Internet of things (IoT) with unmanned aerial vehicles (UAVs). This manuscript not only reviews the use of IoT-enabled unmanned aerial vehicles for inspecting the several construction sites but also emphasizes the utilization of such IoT-enabled autonomous aerial vehicles for ensuring the health and safety measures at the site. It discusses the major limitations and shortcomings of state-of-the-art techniques for the same purpose, i.e., optimization issues in path planning, lightweight artificial intelligence (AI) and computer vision algorithms, coordination in communication using IoT, and scalability of IoT network. Thus, this paper shall help the reader to explore different open research problems in-depth.
41
García Daza, Iván, Mónica Rentero, Carlota Salinas Maldonado, Ruben Izquierdo Gonzalo, Noelia Hernández Parra, Augusto Ballardini, and David Fernandez Llorca. "Fail-Aware LIDAR-Based Odometry for Autonomous Vehicles." Sensors 20, no. 15 (July 23, 2020): 4097. http://dx.doi.org/10.3390/s20154097.
Full textAbstract:
Autonomous driving systems are set to become a reality in transport systems and, so, maximum acceptance is being sought among users. Currently, the most advanced architectures require driver intervention when functional system failures or critical sensor operations take place, presenting problems related to driver state, distractions, fatigue, and other factors that prevent safe control. Therefore, this work presents a redundant, accurate, robust, and scalable LiDAR odometry system with fail-aware system features that can allow other systems to perform a safe stop manoeuvre without driver mediation. All odometry systems have drift error, making it difficult to use them for localisation tasks over extended periods. For this reason, the paper presents an accurate LiDAR odometry system with a fail-aware indicator. This indicator estimates a time window in which the system manages the localisation tasks appropriately. The odometry error is minimised by applying a dynamic 6-DoF model and fusing measures based on the Iterative Closest Points (ICP), environment feature extraction, and Singular Value Decomposition (SVD) methods. The obtained results are promising for two reasons: First, in the KITTI odometry data set, the ranking achieved by the proposed method is twelfth, considering only LiDAR-based methods, where its translation and rotation errors are 1.00 % and 0.0041 deg/m, respectively. Second, the encouraging results of the fail-aware indicator demonstrate the safety of the proposed LiDAR odometry system. The results depict that, in order to achieve an accurate odometry system, complex models and measurement fusion techniques must be used to improve its behaviour. Furthermore, if an odometry system is to be used for redundant localisation features, it must integrate a fail-aware indicator for use in a safe manner.
42
Nine, Julkar, and Rahul Mathavan. "Traffic Light and Back-light Recognition using Deep Learning and Image Processing with Raspberry Pi." Embedded Selforganising Systems 8, no. 2 (December 21, 2021): 15–19. http://dx.doi.org/10.14464/ess.v8i2.490.
Full textAbstract:
Traffic light detection and back-light recognition are essential research topics in the area of intelligent vehicles because they avoid vehicle collision and provide driver safety. Improved detection and semantic clarity may aid in the prevention of traffic accidents by self-driving cars at crowded junctions, thus improving overall driving safety. Complex traffic situations, on the other hand, make it more difficult for algorithms to identify and recognize objects. The latest state-of-the-art algorithms based on Deep Learning and Computer Vision are successfully addressing the majority of real-time problems for autonomous driving, such as detecting traffic signals, traffic signs, and pedestrians. We propose a combination of deep learning and image processing methods while using the MobileNetSSD (deep neural network architecture) model with transfer learning for real-time detection and identification of traffic lights and back-light. This inference model is obtained from frameworks such as Tensor-Flow and Tensor-Flow Lite which is trained on the COCO data. This study investigates the feasibility of executing object detection on the Raspberry Pi 3B+, a widely used embedded computing board. The algorithm’s performance is measured in terms of frames per second (FPS), accuracy, and inference time.
43
Shen, Chao, and Lei Wan. "A Design Methodology for Lithium-Ion Battery Management System and its Application to an Autonomous Underwater Vehicle." Advanced Materials Research 383-390 (November 2011): 7175–82. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.7175.
Full textAbstract:
Battery management system (BMS) in autonomous underwater vehicle (AUV) not only can measure the main parameters of battery packs such as current, voltage, and temperature, but also estimate the state of charge (SOC) of battery packs. This paper proposes a broad approach for the design of battery management system. The new design can improve the cycle life and safety capability. With the model well designed, the parameters required are obtained and the SOC estimation is completed. Extended Kalman filter (EKF) was chosen to make the last estimate with the reliable battery model which was used to the non-linear system to estimate SOC and suitable for AUV applications. The experiments results prove that the data measured by battery management system have high precision and reliability. The estimated error of SOC was also small, which was better than other approaches for estimate.
44
Minenko, Evgen, Olexandr Pyna, Olga Belenchuk, and Tetyana Bondar. "ANALYSIS AND RESULTS OF MEASURES TO ENSURE ROAD SAFETY IN UKRAINE FOR THE PERIOD." Dorogi i mosti 2021, no. 24 (October 1, 2021): 134–48. http://dx.doi.org/10.36100/dorogimosti2021.24.134.
Full textAbstract:
Introduction. Undesirable consequences of fast increase of the vehicle fleet are an increase in the level of accident rate and the number of deaths and injuries in traffic accidents (hereinafter accidents). The positive experience of other countries shows that objectives indicators for reducing the number of traffic accident victims by implementing reasonable measures to eliminate the dominant causes of accidents is the most effective way to achieve the desired result in improving traffic safety. Problem statement. Considering the enormous economic lost caused by traffic accidents, improving of traffic safety has been a priority in the policies of many countries around the world in recent decades. In this regard, the international community pays considerable attention to the development of targeted programs and implementation of traffic safety measures directed at preventing the road traffic injuries. In particular, on March 2, 2010, the UN General Assembly adopted Resolution No. 64/255 «Improving Traffic Safety Worldwide», which proclaimed the 2011–2020 «Decade of Traffic Safety Actions» to reduce deaths from injuries sustained as a result of traffic accident — by 50%. Ukraine, through which seven international transport corridors run [1], did not stay away from solving the common problem and joined in 2011 to the UN initiative regarding reducing the death rate due to traffic accidents by at least 30 % [2]. For evaluation of the achieved result, it is important to analyze the accident statistics and determine whether the planned results were achieved through the implementation of planned measures to improve traffic safety. Purpose. The article considers the dynamics of accident rate and the number of traffic accidents victims in Ukraine for the period 2011–2020, as well as analyzes the main causes of traffic accidents on the public roads, including roads of state importance, to evaluate the outcome of the Decade of Traffic Safety Actions and providing the recommendations for further decreasing of mortality on domestic roads. Materials and methods. The study used the method of statistical processing of data on the number of accidents and their victims in Ukraine and the method of systematic analysis of risk factors that contributed to the accidents. Results. It is determined that the total number of fatalities in traffic accidents for the period 2011–2020 decreased in Ukraine by 27.8 %, and on the public roads — by 44.0 %. However, considering that since 2014 there are no data on traffic accidents in Autonomous Republic of Crimea and partly in Donetsk and Luhansk regions, the indicator of decreasing of deaths per 100 traffic accidents and the result shows more modest achievements: in Ukraine it was possible to reduce deaths by 100 traffic accidents per 100 14 %, and on the public roads — only 5.4 %. Conclusions. Accident rate analysis provides an information basis for understanding the scale of the problem of traffic injuries, evaluating the results of implemented measures to improve traffic safety, the dynamics of positive or negative developments, and allows to plan reasonably the measures to improve road conditions to reduce mortality on roads.
45
Dremliuga, Roman, and Mohd Hazmi bin Mohd Rusli. "The Development of the Legal Framework for Autonomous Shipping: Lessons Learned from a Regulation for a Driverless Car." Journal of Politics and Law 13, no. 3 (August 30, 2020): 295. http://dx.doi.org/10.5539/jpl.v13n3p295.
Full textAbstract:
This article focuses on the regulation of maritime autonomous surface vessels from the perspective of international law of the sea. The article discusses on the possibility of developing a legal framework to regulate autonomous maritime navigation based on laws and regulation of autonomous driving of landed vehicles. The authors opine that existing legal framework does not conform to the goal of regulation of autonomous navigation. However, the regulation of autonomous car testing and exploitation could be imitated to design a new legal framework for autonomous shipping. Despite the divergent approaches, some principles remain in common particularly of cybersecurity and privacy. As computer systems are replacing the need of a master and crew for digitally managed ships, low level of cybersecurity implies an increase in risk of losing control over the vessel. The authors are of the opinion that that current legal acts, standards and their drafts do not pay necessary attention to the problem of cybersecurity of autonomous ships. Moreover, current legislations do not provide mechanisms of influence on behavior of shipowner and shipbuilder to make them apply the best measures. The similar situation is with privacy. Factually, an autonomous ship is a natural tool for surveillance, as to effectively navigate through the seas, it must collect and process information pertaining to navigational safety and other related matters. The question raises how this information has to be collected, kept, processed and deleted. Thus, the maritime community may consider adopting the approach on privacy from regulation for autonomous cars.
46
Alabdulkreem, Eatedal, Jaber Alzahrani, Nadhem Nemri, Olayan Alharbi, Abdullah Mohamed, Radwa Marzouk, and Anwer Hilal. "Computational Intelligence with Wild Horse Optimization Based Object Recognition and Classification Model for Autonomous Driving Systems." Applied Sciences 12, no. 12 (June 20, 2022): 6249. http://dx.doi.org/10.3390/app12126249.
Full textAbstract:
Presently, autonomous systems have gained considerable attention in several fields such as transportation, healthcare, autonomous driving, logistics, etc. It is highly needed to ensure the safe operations of the autonomous system before launching it to the general public. Since the design of a completely autonomous system is a challenging process, perception and decision-making act as vital parts. The effective detection of objects on the road under varying scenarios can considerably enhance the safety of autonomous driving. The recently developed computational intelligence (CI) and deep learning models help to effectively design the object detection algorithms for environment perception depending upon the camera system that exists in the autonomous driving systems. With this motivation, this study designed a novel computational intelligence with a wild horse optimization-based object recognition and classification (CIWHO-ORC) model for autonomous driving systems. The proposed CIWHO-ORC technique intends to effectively identify the presence of multiple static and dynamic objects such as vehicles, pedestrians, signboards, etc. Additionally, the CIWHO-ORC technique involves the design of a krill herd (KH) algorithm with a multi-scale Faster RCNN model for the detection of objects. In addition, a wild horse optimizer (WHO) with an online sequential ridge regression (OSRR) model was applied for the classification of recognized objects. The experimental analysis of the CIWHO-ORC technique is validated using benchmark datasets, and the obtained results demonstrate the promising outcome of the CIWHO-ORC technique in terms of several measures.
47
Carballo, Alexander, Shunya Seiya, Jacob Lambert, Hatem Darweesh, Patiphon Narksri, Luis Yoichi Morales, Naoki Akai, Eijiro Takeuchi, and Kazuya Takeda. "End-to-End Autonomous Mobile Robot Navigation with Model-Based System Support." Journal of Robotics and Mechatronics 30, no. 4 (August 20, 2018): 563–83. http://dx.doi.org/10.20965/jrm.2018.p0563.
Full textAbstract:
Autonomous mobile robot navigation in real unmodified outdoor areas frequented by people on their business, children playing, fast running bicycles, and even robots, remains a difficult challenge. For eleven years, the Tsukuba Challenge Real World Robot Challenge (RWRC) has brought together robots, researchers, companies, government, and ordinary citizens, under the same outdoor space to push forward the limits of autonomous mobile robots. For the Tsukuba Challenge 2017 participation, our team proposed to study the problem of sensors-to-actuators navigation (also called End-to-End), this is, having the robot to navigate towards the destination on a complex path, not only moving straight but also turning at intersections. End-to-End (E2E) navigation was implemented using a convolutional neural network (CNN): the robot learns how to go straight, turn left, and turn right, using camera images and trajectory data. E2E network training and evaluation was performed at Nagoya University, on similar outdoor conditions to that of Tsukuba Challenge 2017 (TC2017). Even thought E2E was trained on a different environment and conditions, the robot successfully followed the designated trajectory in the TC2017 course. Learning how to follow the road no matter the environment is of the key attributes of E2E based navigation. Our E2E does not perform obstacle avoidance and can be affected by illumination and seasonal changes. Therefore, to improve safety and add fault tolerance measures, we developed an E2E navigation approach with model-based system as backup. The model-based system is based on our open source autonomous vehicle software adapted to use on a mobile robot. In this work we describe our approach, implementation, experiences and main contributions.
48
Omar, Priyanka, MVN Siva Kumar, and Subbarao Yarramsetty. "Management of Various Safety Parameters in Tunnel Construction: A Critical Review." E3S Web of Conferences 170 (2020): 06003. http://dx.doi.org/10.1051/e3sconf/202017006003.
Full textAbstract:
Population is growing rapidly and so are vehicles on roads. This leads to enormous need of tunnels and subways for easy locomotion and mobilization. From conventional tunnels, for sewage and water facilities to modern electrified tunnels, for mass transit and underwater highways, method of construction of a tunnel to its safety inspection and maintenance has taken a wide leap. Tunnel construction requires wide range of resources like human, technology, machinery, materials, energy, and finance. To handle all these resources simultaneously becomes complex and requires good management. Even with good management, accidents and hazards might occur. A critical review of various safety parameters while constructing a tunnel and its management is presented in this paper. This review paper discusses the history of safety of tunnels till present safety measures and techniques adopted around the world for different tunnels; various critical factors which affect the safety in tunnels; the extent of damage occurring due to these factors; numerous preventive measures which are adopted around the world to prevent loss of property and lives in tunnels; and advanced technology and software, which are being used in modern era to enhance safety in tunnel construction. The study from manual horse drawn-tramway to autonomous robotic system has been done. This paper also considers various tunnels around the world and gives a summary of factors for safety focused on for making these tunnels, with its adopted remedy.
49
Pawlik, Marek. "Concept of the railway safety, security and cybersecurity functional integrity levels." MATEC Web of Conferences 294 (2019): 03003. http://dx.doi.org/10.1051/matecconf/201929403003.
Full textAbstract:
Observed growing changes in the character and severity of the risks in rail traffic safety and rail transport security are associated with present development of utilized technical solutions. New hazards are coming out, besides known ones, including hazards associated with cyber-crime. As a result it is fully justified to undertake works dedicated to collect and settle all risks associated with technical solutions using modern technologies for acquisition, computing and transfer of the data, which are vital from the rail traffic safety and rail transport security point of view. Article defines rail transport systems safety, security and cybersecurity functional integrity levels thanks to knock-out and differentiating questions regarding identified key safety related functionalities. Proposed methodology was used for safety and security verification of a chosen homogenous rail transport system separated from the overall Polish railway system. Results have shown discrepancies in utilized protection measures. Proposed methodology can be used for assessment of existing systems as well as for specifying scopes of investments both for infrastructure and rolling stock modernizations. Applicability range covers railway transport, light rail services, metro, urban rail transport systems as well as rail based transport systems using autonomous vehicles.
50
Molero, Gemma Dolores, Sara Poveda-Reyes, Ashwani Kumar Malviya, Elena García-Jiménez, Maria Chiara Leva, and Francisco Enrique Santarremigia. "Computational Solutions Based on Bayesian Networks to Hierarchize and to Predict Factors Influencing Gender Fairness in the Transport System: Four Use Cases." Sustainability 13, no. 20 (October 14, 2021): 11372. http://dx.doi.org/10.3390/su132011372.
Full textAbstract:
Previous studies have highlighted inequalities and gender differences in the transport system. Some factors or fairness characteristics (FCs) strongly influence gender fairness in the transport system. The difference with previous studies, which focus on general concepts, is the incorporation of level 3 FCs, which are more detailed aspects or measures that can be implemented by companies or infrastructure managers and operators in order to increase fairness and inclusion in each use case. The aim of this paper is to find computational solutions, Bayesian networks, and analytic hierarchy processes capable of hierarchizing level 3 FCs and to predict by simulation their values in the case of applying some improvements. This methodology was applied to data from women in four use cases: railway transport, autonomous vehicles, bicycle sharing stations, and transport employment. The results showed that fairer railway transport requires increased personal space, hospitality rooms, help points, and helpline numbers. For autonomous vehicles, the perception of safety, security, and sustainability should be increased. The priorities for bicycle sharing stations are safer cycling paths avoiding hilly terrains and introducing electric bicycles, child seats, or trailers to carry cargo. In transport employment, the priorities are fair recruitment and promotion processes and the development of family-friendly policies.