Journal articles on the topic 'Safe urban driving'
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1
Bhattacharya, Shelley, and Kristina Diaz. "Driving Habits of Older Adults." Kansas Journal of Medicine 5, no. 4 (November 27, 2012): 134–41. http://dx.doi.org/10.17161/kjm.v5i4.11423.
Abstract:
BACKGROUND: The older adult population is the fastest growing cohort in Kansas, resulting in a growing number of older drivers. With age, changes in the ability to drive can compromise safety. Although it is challenging for health care providers to identify unsafe older drivers, it would be helpful to know what common driving habits they share. This exploratory study evaluated differences in the self-reported driving behaviors of older drivers in urban and rural settings of Kansas. METHODS: A one-page, 19-item survey was administered to patients over age 65 in the waiting rooms of two physician medical offices in urban Kansas City and rural Junction City, Kansas. RESULTS: A total of 105 surveys were completed. Rural drivers reported they were involved in approximately 9% more accidents than the urban drivers (p = 0.166). Rural drivers were more likely to drive in poor weather conditions, such as snow, ice, fog, and rain (p = 0.032). Eyeglasses were worn by 10% of the rural cohort compared to 37.8% of the urban cohort (p = 0.0044). More urban drivers reported they did not want to make changes to their current driving habits (71% vs 40%; p = 0.004). Urban drivers drove a longer distance to reach their destinations. Drivers from both environments avoided unfamiliar roads and did not use cell phones or global positioning system (GPS) devices while driving. CONCLUSIONS: By understanding the habits of older drivers, healthcare providers can tailor safe driving messages to support safe driving and enhance patient safety. Physicians could benefit from knowing that older rural drivers wore their glasses less frequently, trended towards having more accidents, and were more prone to drive during inclement weather. Urban Kansas drivers drove further to get to their destinations than their rural Kansas counterparts. Understanding these driving habits and tailoring their prevention messages accordingly may help health care providers in Kansas improve older patient’s safe driving behaviors.
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Rafi'ah, Rafi'ah, Iga Maliga, Asri Reni Handayani, Ana Lestari, and Herni Hasifah. "Analysis of the Influence of Perception on Safety Riding Behavior in the Sumbawa Community." Jurnal Penelitian Pendidikan IPA 9, no. 8 (August 25, 2023): 6675–81. http://dx.doi.org/10.29303/jppipa.v9i8.4775.
Abstract:
Unsafe driving behavior can have several negative impacts on society. Motorcyclists who exhibit safe and law-abiding driving behaviors are essential in minimizing the risk of accidents on the road. Perception plays a crucial role in driving safety, as it allows individuals to interpret and comprehend information received through their senses. This ability is crucial in identifying potential hazards and making quick decisions to avoid accidents. This study aims to analyze the influence of perceptions on safe driving behavior. Additionally, it observes the driving behavior of motorcyclists between urban and rural areas in Sumbawa. The research adopts a quantitative approach with a comparative study design. The sample consists of 100 respondents selected through purposive sampling and divided into two regions. The statistical analysis using simple linear regression in SPSS version 16.0 shows a significance value of 0.04 with an 8.1% influence for perception on safe driving behavior. The T-test results for safety riding observations in urban and rural areas in Sumbawa indicate a T-test of 0.886. In conclusion, there is a significant but very weak influence between perception and safe driving behavior. The observation results show no difference in driving behavior between urban and rural areas in Sumbawa
3
Farag, Wael. "Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks." Recent Patents on Computer Science 12, no. 2 (February 25, 2019): 120–27. http://dx.doi.org/10.2174/2213275911666181106160002.
Abstract:
Background: In this paper, a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering manoeuvring, is proposed as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. Methods: This data is then used to train the proposed CNN to facilitate what it is called “Behavioral Cloning”. The proposed Behavior Cloning CNN is named as “BCNet”, and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam’s optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. Results: The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. Conclusion: The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.
4
Xu, Hui, and Jianping Wu. "What Road Elements are More Important than Others for Safe Driving on Urban Roads?" Promet - Traffic&Transportation 35, no. 6 (December 20, 2023): 814–28. http://dx.doi.org/10.7307/ptt.v35i6.394.
Abstract:
Road elements are increasingly digitalized to provide drivers advanced assistance especially in the emergent or adverse conditions. It is challenging and expensive to accurately digitalize all the road elements especially on the urban roads with many infrastructures and complex designs, where we may focus on the most important ones at the first stage. This research designs a questionnaire to ask the drivers to rank the importance of the road elements in various driving conditions. Driver characteristics are also collected, including age, driving style, accident experience, and accumulated driving distance, to explore their effect on drivers’ cognition of road elements importance. It is found that driving is a complex activity, and the moving elements (e.g. surrounding cars) are more important than the non-moving ones. Attention should be paid to the road elements even distant from the ego car, to get prepared to the potential driving risk or penalty. Statistical difference between the experienced and non-experienced drivers recommends that driver assistance system should be sufficiently trained in various conditions, to build up autonomous driving tactics and skills. This research promotes the understanding of driving cognition pattern to provide insights into the development of road digitalization.
5
Arshad, Saba, Muhammad Sualeh, Dohyeong Kim, Dinh Van Nam, and Gon-Woo Kim. "Clothoid: An Integrated Hierarchical Framework for Autonomous Driving in a Dynamic Urban Environment." Sensors 20, no. 18 (September 5, 2020): 5053. http://dx.doi.org/10.3390/s20185053.
Abstract:
In recent years, research and development of autonomous driving technology have gained much interest. Many autonomous driving frameworks have been developed in the past. However, building a safely operating fully functional autonomous driving framework is still a challenge. Several accidents have been occurred with autonomous vehicles, including Tesla and Volvo XC90, resulting in serious personal injuries and death. One of the major reasons is the increase in urbanization and mobility demands. The autonomous vehicle is expected to increase road safety while reducing road accidents that occur due to human errors. The accurate sensing of the environment and safe driving under various scenarios must be ensured to achieve the highest level of autonomy. This research presents Clothoid, a unified framework for fully autonomous vehicles, that integrates the modules of HD mapping, localization, environmental perception, path planning, and control while considering the safety, comfort, and scalability in the real traffic environment. The proposed framework enables obstacle avoidance, pedestrian safety, object detection, road blockage avoidance, path planning for single-lane and multi-lane routes, and safe driving of vehicles throughout the journey. The performance of each module has been validated in K-City under multiple scenarios where Clothoid has been driven safely from the starting point to the goal point. The vehicle was one of the top five to successfully finish the autonomous vehicle challenge (AVC) in the Hyundai AVC.
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Wang, Shaobo, Pan Zhao, Biao Yu, Weixin Huang, and Huawei Liang. "Vehicle Trajectory Prediction by Knowledge-Driven LSTM Network in Urban Environments." Journal of Advanced Transportation 2020 (November 7, 2020): 1–20. http://dx.doi.org/10.1155/2020/8894060.
Abstract:
An accurate prediction of future trajectories of surrounding vehicles can ensure safe and reasonable interaction between intelligent vehicles and other types of vehicles. Vehicle trajectories are not only constrained by a priori knowledge about road structure, traffic signs, and traffic rules but also affected by posterior knowledge about different driving styles of drivers. The existing prediction models cannot fully combine the prior and posterior knowledge in the driving scene and perform well only in a specific traffic scenario. This paper presents a long short-term memory (LSTM) neural network driven by knowledge. First, a driving knowledge base is constructed to describe the prior knowledge about a driving scenario. Then, the prediction reference baseline (PRB) based on driving knowledge base is determined by using the rule-based online reasoning system. Finally, the future trajectory of the target vehicle is predicted by an LSTM neural network based on the prediction reference baseline, while the predicted trajectory considers both posterior and prior knowledge without increasing the computation complexity. The experimental results show that the proposed trajectory prediction model can adapt to different driving scenarios and predict trajectories with high accuracy due to the unique combination of the prior and posterior knowledge in the driving scene.
7
Urmson, Chris, Chris Baker, John Dolan, Paul Rybski, Bryan Salesky, William Whittaker, Dave Ferguson, and Michael Darms. "Autonomous Driving in Traffic: Boss and the Urban Challenge." AI Magazine 30, no. 2 (February 26, 2009): 17. http://dx.doi.org/10.1609/aimag.v30i2.2238.
Abstract:
The DARPA Urban Challenge was a competition to develop autonomous vehicles capable of safely, reliably and robustly driving in traffic. In this article we introduce Boss, the autonomous vehicle that won the challenge. Boss is complex artificially intelligent software system embodied in a 2007 Chevy Tahoe. To navigate safely, the vehicle builds a model of the world around it in real time. This model is used to generate safe routes and motion plans in both on roads and in unstructured zones. An essential part of Boss’ success stems from its ability to safely handle both abnormal situations and system glitches.
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Inder, Silva, and Shi. "Learning Control Policies of Driverless Vehicles from UAV Video Streams in Complex Urban Environments." Remote Sensing 11, no. 23 (November 20, 2019): 2723. http://dx.doi.org/10.3390/rs11232723.
Abstract:
The way we drive, and the transport of today are going through radical changes. Intelligent mobility envisions to improve the efficiency of traditional transportation through advanced digital technologies, such as robotics, artificial intelligence and Internet of Things. Central to the development of intelligent mobility technology is the emergence of connected autonomous vehicles (CAVs) where vehicles are capable of navigating environments autonomously. For this to be achieved, autonomous vehicles must be safe, trusted by passengers, and other drivers. However, it is practically impossible to train autonomous vehicles with all the possible traffic conditions that they may encounter. The work in this paper presents an alternative solution of using infrastructure to aid CAVs to learn driving policies, specifically for complex junctions, which require local experience and knowledge to handle. The proposal is to learn safe driving policies through data-driven imitation learning of human-driven vehicles at a junction utilizing data captured from surveillance devices about vehicle movements at the junction. The proposed framework is demonstrated by processing video datasets captured from uncrewed aerial vehicles (UAVs) from three intersections around Europe which contain vehicle trajectories. An imitation learning algorithm based on long short-term memory (LSTM) neural network is proposed to learn and predict safe trajectories of vehicles. The proposed framework can be used for many purposes in intelligent mobility, such as augmenting the intelligent control algorithms in driverless vehicles, benchmarking driver behavior for insurance purposes, and for providing insights to city planning.
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Liu, Yi, Ming Jian Yu, and Ke Si You. "A Study on the Lane Width of Car-Only Urban Underground Road." Advanced Materials Research 838-841 (November 2013): 1191–96. http://dx.doi.org/10.4028/www.scientific.net/amr.838-841.1191.
Abstract:
Theoretical analysis combined with experimental study was conducted to determine the proper lane width for urban car-only underground road. Vehicle trajectory data were collected in the experiment using three different ways including naturalistic driving experiment, video image processing and driving simulation. Lateral offset of each vehicle moving on the lane were obtained to determine the lane width. The results shows that for the car-only underground road, the design vehicle width is 1.8m,considering the safety margin and driving comfort, the minimum lane width 3m and 3.25m are reasonable and safe for the design speed 60km/h and 80km/h respectively.
10
Vadivelu, A., Mamidipaka Sai Roshini, and Yamali Sravya. "Fine-Grained Multi-class Road Segmentation using MultiScale Probability Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (May 31, 2024): 1775–80. http://dx.doi.org/10.22214/ijraset.2024.61924.
Abstract:
Abstract: Driven road segmentation, a crucial part of advanced driver assistance systems, looks at the surroundings to keep vehicles within safe driving limits (ADASs). It begins by outlining the automatic lane detection shortfalls of conventional computer vision systems, pointing out problems such subpar segmentation, insufficient mask edge contours, sluggish processing, and restricted flexibility in intricate urban environments. Next, a multi-step procedure using deep learning networks is offered as a solution. This involves extracting vector skeletons, computing neighbouring pixels, assigning proportional weights depending on endpoints, and getting binary prediction masks. The conversation also touches on how self-driving technology will affect society, emphasizing how it could provide safe and intelligent transportation choices in the face of an increase in traffic accidents caused by careless drivers. Self-driving cars could be the first practical example of socially conscious robots interacting with humans, the story implies, even if it acknowledges possible public opposition. Furthermore, the story highlights the latest developments in autonomous driving technology, highlighting the vital requirement for strong sensing, perception, and cognitive technologies to enable completely autonomous vehicles that can adjust to changing road conditions.
11
Godoy, Jorge, Joshué Pérez, Enrique Onieva, Jorge Villagrá, Vicente Milanés, and Rodolfo Haber. "A DRIVERLESS VEHICLE DEMONSTRATION ON MOTORWAYS AND IN URBAN ENVIRONMENTS." TRANSPORT 30, no. 3 (January 28, 2015): 253–63. http://dx.doi.org/10.3846/16484142.2014.1003406.
Abstract:
The constant growth of the number of vehicles in today’s world demands improvements in the safety and efficiency of roads and road use. This can be in part satisfied by the implementation of autonomous driving systems because of their greater precision than human drivers in controlling a vehicle. As result, the capacity of the roads would be increased by reducing the spacing between vehicles. Moreover, greener driving modes could be applied so that the fuel consumption, and therefore carbon emissions, would be reduced. This paper presents the results obtained by the AUTOPIA program during a public demonstration performed in June 2012. This driverless experiment consisted of a 100-kilometre route around Madrid (Spain), including both urban and motorway environments. A first vehicle – acting as leader and manually driven – transmitted its relevant information – i.e., position and speed – through an 802.11p communication link to a second vehicle, which tracked the leader’s trajectory and speed while maintaining a safe distance. The results were encouraging, and showed the viability of the AUTOPIA approach.
12
Pulvirenti, Giulia, Natalia Distefano, Salvatore Leonardi, and Tomaz Tollazzi. "Are Double-Lane Roundabouts Safe Enough? A CHAID Analysis of Unsafe Driving Behaviors." Safety 7, no. 1 (March 8, 2021): 20. http://dx.doi.org/10.3390/safety7010020.
Abstract:
This study investigated the nature and causes of unsafe driving behavior at roundabouts through an on-road study. Four urban double-lane roundabouts with different layouts were selected for an on-road study. Sixty-six drivers (41 males and 25 females) aged 18–65 years took part in the study. Unsafe behaviors observed during the in situ survey were divided into three different categories: entry unsafe behaviors, circulation unsafe behaviors, and exit unsafe behaviors. Three chi-square automatic interaction detection (CHAID) analyses were developed in order to analyze the influence of roundabout characteristics and maneuvers on unsafe behaviors at double-lane roundabouts. The results confirmed the awareness that double-lane roundabouts are unsafe and inadvisable. More than half of unsafe driving behaviors were found to be entry unsafe behaviors. Moreover, the entry radius was found to be the geometric variable most influencing unsafe driving behaviors.
13
Wu, Li Xin, and Guo Zhu Cheng. "Speed Management on Icy and Snowy Pavement of Urban Road." Advanced Materials Research 225-226 (April 2011): 593–96. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.593.
Abstract:
Icy and snowy pavement create obvious adverse impacts on road safety so that speed management is necessary for safety. According to the analysis of driver’s brake reaction time and vehicle’s brake distance on icy and snowy pavement, automobile’s parking distances were calculated and given under the condition of different speed and icy and snowy pavement types through observed friction coefficient data of snowy pavement, rough icy pavement, smooth icy pavement and icy-snowy pavement. The calculation method of maximum safe speeds under the condition of different icy and snowy pavement types and traffic volume were put forward. And suggestion values of car’s maximum safe speed when driving on icy and snowy pavement were given.
14
Van Liempt, Ilse, and Irina Van Aalst. "Urban Surveillance and the Struggle between Safe and Exciting Nightlife Districts." Surveillance & Society 9, no. 3 (March 27, 2012): 280–92. http://dx.doi.org/10.24908/ss.v9i3.4270.
Abstract:
Cities attract vast numbers of people at night (Roberts and Eldridge 2009). In recent decades the evening economy has started to play a significant role in city centre regeneration, with alcohol related establishments as the driving force (Hollands and Chatterton 2003). Concerns about personal safety and fear of crime have become central in determining the success of these leisure-based inner-city areas (Judd 2003, Bannister et al. 2006). This attitude is also reflected in academic work, where most studies explore the late night economy in terms of alcohol consumption, disorder and anti-social behaviour (Hobbs et al. 2003, Hadfield 2006, Monaghan 2002, Plant and Plant 2006, Winlow and Hall 2006). Nightlife districts are, however, favoured by visitors for their adventure and excitement (Hubbard 2005). The question raised in this paper is how surveillance measures in different nightlife districts are legitimized, taking into account the fact that these districts need not only to be safe but also stimulating. Based on an analysis of policy documents, nighttime observations and expert interviews with stakeholders in the Safe Nightlife Programmes of Rotterdam and Utrecht, different local safety measures, their legitimizations and their outcomes in different local urban settings will be analysed.
15
Van, Nam Dinh, Muhammad Sualeh, Dohyeong Kim, and Gon-Woo Kim. "A Hierarchical Control System for Autonomous Driving towards Urban Challenges." Applied Sciences 10, no. 10 (May 20, 2020): 3543. http://dx.doi.org/10.3390/app10103543.
Abstract:
In recent years, the self-driving car technologies have been developed with many successful stories in both academia and industry. The challenge for autonomous vehicles is the requirement of operating accurately and robustly in the urban environment. This paper focuses on how to efficiently solve the hierarchical control system of a self-driving car into practice. This technique is composed of decision making, local path planning and control. An ego vehicle is navigated by global path planning with the aid of a High Definition map. Firstly, we propose the decision making for motion planning by applying a two-stage Finite State Machine to manipulate mission planning and control states. Furthermore, we implement a real-time hybrid A* algorithm with an occupancy grid map to find an efficient route for obstacle avoidance. Secondly, the local path planning is conducted to generate a safe and comfortable trajectory in unstructured scenarios. Herein, we solve an optimization problem with nonlinear constraints to optimize the sum of jerks for a smooth drive. In addition, controllers are designed by using the pure pursuit algorithm and the scheduled feedforward PI controller for lateral and longitudinal direction, respectively. The experimental results show that the proposed framework can operate efficiently in the urban scenario.
16
Karthikeyan, P., Wei-Lun Chen, and Pao-Ann Hsiung. "Autonomous Intersection Management by Using Reinforcement Learning." Algorithms 15, no. 9 (September 13, 2022): 326. http://dx.doi.org/10.3390/a15090326.
Abstract:
Developing a safer and more effective intersection-control system is essential given the trends of rising populations and vehicle numbers. Additionally, as vehicle communication and self-driving technologies evolve, we may create a more intelligent control system to reduce traffic accidents. We recommend deep reinforcement learning-inspired autonomous intersection management (DRLAIM) to improve traffic environment efficiency and safety. The three primary models used in this methodology are the priority assignment model, the intersection-control model learning, and safe brake control. The brake-safe control module is utilized to make sure that each vehicle travels safely, and we train the system to acquire an effective model by using reinforcement learning. We have simulated our proposed method by using a simulation of urban mobility tools. Experimental results show that our approach outperforms the traditional method.
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Dharmasena, Shameen Randika, and Edirisooriya Arachchige Tharanga Suresh. "A METHODOLOGY TO ANALYZE ROAD LANDSCAPE IN ACCIDENT BLACK-SPOTS: THE CASE OF SOUTHERN EXPRESSWAY, SRI LANKA." International Journal of Architectural Research: ArchNet-IJAR 12, no. 2 (August 2, 2018): 347. http://dx.doi.org/10.26687/archnet-ijar.v12i2.1547.
Abstract:
The Road Landscape creates the character and the spatial quality for safe driving. It is evident that the spatial qualities of the road landscape have impacted on road accidents once the field data is analyzed. Identification of accident-prone areas (Black-Spots) is the vital factor for road safety management process. The study focused on to developing a methodology to visually analyze road landscape with using identified Black-Spots in Southern Expressway, Sri Lanka. Data is collected and analyzed as two phases; one is from recorded accidents data and other from a live recording of the expressway driving stretch. This study highlighted the possibilities of analyzing the identified ‘Black-spots’ by using Photo-Fixation method. In conclusion, the study emphasizes the possibility of using a qualitative methodology to analyze the road landscape through spatial characteristics; which can be developed up to a more advanced level to identifying driving behavior related accidents and to take migratory actions.
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Randell, Nastassia J. S., Samuel G. Charlton, and Nicola J. Starkey. "Driving With ADHD: Performance Effects and Environment Demand in Traffic." Journal of Attention Disorders 24, no. 11 (July 9, 2016): 1570–80. http://dx.doi.org/10.1177/1087054716658126.
Abstract:
Objective: This research investigated the on-road driving performance of individuals with ADHD across a range of road and traffic conditions to determine whether errors were linked to situational complexity and attentional demands. Method: The everyday driving performance of medicated drivers with ADHD, unmedicated drivers with ADHD, and controls was tested in urban, residential, rural, and highway environments using driver license testing procedures. Results: Unmedicated drivers with ADHD displayed fewer safe driving skills and committed more inattentive and impatient driving errors, particularly in low demand highway and rural driving conditions. Medicated drivers’ performance was not reliably different than controls. Participants in both ADHD groups were more likely than controls to report risky driving and involvement in crashes. Conclusion: The results demonstrate that situations with low attentional demand are particularly risky for unmedicated ADHD drivers and suggest that focus on these situations may be useful in improving driving outcomes for this population.
19
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.
Abstract:
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.
20
Mo, Yanghui, Roshan Vijay, Raphael Rufus, Niels de Boer, Jungdae Kim, and Minsang Yu. "Enhanced Perception for Autonomous Vehicles at Obstructed Intersections: An Implementation of Vehicle to Infrastructure (V2I) Collaboration." Sensors 24, no. 3 (January 31, 2024): 936. http://dx.doi.org/10.3390/s24030936.
Abstract:
In urban intersections, the sensory capabilities of autonomous vehicles (AVs) are often hindered by visual obstructions, posing significant challenges to their robust and safe operation. This paper presents an implementation study focused on enhancing the safety and robustness of Connected Automated Vehicles (CAVs) in scenarios with occluded visibility at urban intersections. A novel LiDAR Infrastructure System is established for roadside sensing, combined with Baidu Apollo’s Automated Driving System (ADS) and Cohda Wireless V2X communication hardware, and an integrated platform is established for roadside perception enhancement in autonomous driving. The field tests were conducted at the Singapore CETRAN (Centre of Excellence for Testing & Research of Autonomous Vehicles—NTU) autonomous vehicle test track, with the communication protocol adhering to SAE J2735 V2X communication standards. Communication latency and packet delivery ratio were analyzed as the evaluation metrics. The test results showed that the system can help CAV detect obstacles in advance under urban occluded scenarios.
21
Alighanbari, Sina, and Nasser L. Azad. "Safe Adaptive Deep Reinforcement Learning for Autonomous Driving in Urban Environments. Additional Filter? How and Where?" IEEE Access 9 (2021): 141347–59. http://dx.doi.org/10.1109/access.2021.3119915.
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Torkamannejad Sabzevari, Javad, Amir Reza Nabipour, Narges Khanjani, Ali Molaei Tajkooh, and Mark J. M. Sullman. "An observational study of secondary task engagement while driving on urban streets in Iranian Safe Communities." Accident Analysis & Prevention 96 (November 2016): 56–63. http://dx.doi.org/10.1016/j.aap.2016.07.020.
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Chen, Jing, Cong Zhao, Shengchuan Jiang, Xinyuan Zhang, Zhongxin Li, and Yuchuan Du. "Safe, Efficient, and Comfortable Autonomous Driving Based on Cooperative Vehicle Infrastructure System." International Journal of Environmental Research and Public Health 20, no. 1 (January 3, 2023): 893. http://dx.doi.org/10.3390/ijerph20010893.
Abstract:
Traffic crashes, heavy congestion, and discomfort often occur on rough pavements due to human drivers’ imperfect decision-making for vehicle control. Autonomous vehicles (AVs) will flood onto urban roads to replace human drivers and improve driving performance in the near future. With the development of the cooperative vehicle infrastructure system (CVIS), multi-source road and traffic information can be collected by onboard or roadside sensors and integrated into a cloud. The information is updated and used for decision-making in real-time. This study proposes an intelligent speed control approach for AVs in CVISs using deep reinforcement learning (DRL) to improve safety, efficiency, and ride comfort. First, the irregular and fluctuating road profiles of rough pavements are represented by maximum comfortable speeds on segments via vertical comfort evaluation. A DRL-based speed control model is then designed to learn safe, efficient, and comfortable car-following behavior based on road and traffic information. Specifically, the model is trained and tested in a stochastic environment using data sampled from 1341 car-following events collected in California and 110 rough pavements detected in Shanghai. The experimental results show that the DRL-based speed control model can improve computational efficiency, driving efficiency, longitudinal comfort, and vertical comfort in cars by 93.47%, 26.99%, 58.33%, and 6.05%, respectively, compared to a model predictive control-based adaptive cruise control. The results indicate that the proposed intelligent speed control approach for AVs is effective on rough pavements and has excellent potential for practical application.
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Sun, Wei, and Kannan Srinivasan. "On the Feasibility of Securing Vehicle-Pavement Interaction." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 1 (March 29, 2022): 1–24. http://dx.doi.org/10.1145/3517230.
Abstract:
Road surface information (e.g., smooth road or bumpy road with potholes or bumps) is important for safe driving (i.e., it's necessary to be aware of the road surface conditions during driving). However, the high-cost sensor (e.g., LiDAR and camera) based road surface sensing approaches cannot work properly in inclement weather conditions (e.g., fogging and snowing) due to the line-of-sight requirement. The low-cost and ubiquitous smartphone-based road surface sensing approach is not reliable and safe to use, since it relies on the vibration of the vehicle body to sense the road surface (i.e., the vehicle's tires need to touch the bumps on the road surface). Can we automate the contact-free road surface sensing with low-cost sensors for safe driving without requiring the vehicle's tires to touch the bumps on the road surface? In this paper, we propose Tago, a system that can achieve contact-free road surface sensing with commodity passive RFID tags. Instead of deploying RFID tags or readers along the road or lamp post (i.e., infrastructure-based deployment), we deploy the reader inside of the vehicle and attach the tag and the reader's antenna at the front end of the vehicle like the vehicle's headlights (i.e., infrastructure-free deployment). However, there is a great challenge to obtain the clean reflection from the road surface, since the reflection may be drown in the backscattered signals due to multipath effect. Moreover, it is not reliable to use the composite signals received at the reader to sense the road surface conditions. Therefore, we first comprehensively analyse the variation of composite signals received at the reader. Then, we propose a signal cancellation approach to extract the clean reflections from the road surface, such that we can accurately sense the road surface conditions for safe driving. Our experiments with different vehicles (e.g., Honda Civic Frankenfish, Folsom, Flutter and CR-V Warner) driven on different roadways (e.g., urban and residential area) show that Tago can effectively sense the road surface information.
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Essien, Etido. "Impacts of Governance toward Sustainable Urbanization in a Midsized City: A Case Study of Uyo, Nigeria." Land 11, no. 1 (December 27, 2021): 37. http://dx.doi.org/10.3390/land11010037.
Abstract:
Urban studies in Nigeria mostly focus on large cities and metropolitan areas, with minimal attention given to sustainable urban development in midsized cities. In this study, we address this knowledge gap and examine the policies and practices driving urban growth in Uyo, a midsized city in Nigeria. Specifically, we evaluate to what extent the prevailing urban governance culture and practices move the city toward or away from being inclusive, safe, resilient, and sustainable—central tenets of UN Sustainable Development Goal (SDG) 11. This study critically explores the strategic and operational approaches deployed by public stakeholders in pursuit of urban development, housing security, and economic and infrastructure development. We find the lack of continuity in commitment to urban infrastructural development projects and a flawed land tenure system that exacerbates housing insecurity are the two most critical challenges to address in attaining the goals of SDG11 in Uyo. The former calls for better fiscal management and adoption of good governance practices across the administrative hierarchy. The land tenure system can be made equitable and less cumbersome by overhauling the 1999 Land Use Act law of the country. Our findings can inform policies to make midsized cities facing similar challenges more inclusive, safe, resilient, and sustainable.
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Martin, Matthew S., Brandon Huard-Nicholls, and Aaron P. Johnson. "Gaze and pupil size variability predict difficulty-level and safe intersection crosses in a driving simulator." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 65, no. 1 (September 2021): 843–47. http://dx.doi.org/10.1177/1071181321651289.
Abstract:
Western populations are ageing. With age comes an increased risk of mild cognitive impairment (MCI) and fragility that leads to higher fatal car crashes. This study develops a driving simulation paradigm that seeks to detect unsafe drivers, particularly among older drivers with MCI. The paradigm includes repeated urban intersection crossings at three difficulty levels while eye movements are tracked. The internal validity of this part of the paradigm was tested with young adults ( N = 7). Results indicated that the simulator tests elicited unsafe driving behaviors that varied across difficulty and avoided ceiling and floor effects. Eye movement metrics associated with cognitive load also varied with difficulty and predicted safe crosses. The strongest predictors were gaze transition entropy, gaze variability, and pupil size entropy. These findings indicate internal validity of the tests. Future research should test the external validity of this paradigm with a larger, more diverse sample.
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Noori, Amani Y., Dr Shaimaa H. Shaker, and Dr Raghad Abdulaali Azeez. "Semantic Segmentation of Urban Street Scenes Using Deep Learning." Webology 19, no. 1 (January 20, 2022): 2294–306. http://dx.doi.org/10.14704/web/v19i1/web19156.
Abstract:
Scene classification is essential conception task used by robotics for understanding the environmental. The outdoor scene like urban street scene is composing of image with depth having greater variety than iconic object image. The semantic segmentation is an important task for autonomous driving and mobile robotics applications because it introduces enormous information need for safe navigation and complex reasoning. This paper introduces a model for classification all pixel’s image and predicates the right object that contains this pixel. This model adapts famous network image classification VGG16 with fully convolution network (FCN-8) and transfer learned representation by fine tuning for doing segmentation. Skip Architecture is added between layers to combine coarse, semantic, and local appearance information to generate accurate segmentation. This model is robust and efficiency because it efficient consumes low memory and faster inference time for testing and training on Camvid dataset. The output module is designed by using a special computer equipped by GPU memory NVIDIA GeForce RTX 2060 6G, and programmed by using python 3.7 programming language. The proposed system reached an accuracy 0.8804 and MIOU 73% on Camvid dataset.
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Medina-Lee, Juan, Antonio Artuñedo, Jorge Godoy, and Jorge Villagra. "Merit-Based Motion Planning for Autonomous Vehicles in Urban Scenarios." Sensors 21, no. 11 (May 28, 2021): 3755. http://dx.doi.org/10.3390/s21113755.
Abstract:
Safe and adaptable motion planning for autonomous vehicles remains an open problem in urban environments, where the variability of situations and behaviors may become intractable using rule-based approaches. This work proposes a use-case-independent motion planning algorithm that generates a set of possible trajectories and selects the best of them according to a merit function that combines longitudinal comfort, lateral comfort, safety and utility criteria. The system was tested in urban scenarios on simulated and real environments, and the results show that different driving styles can be achieved according to the priorities set in the merit function, always meeting safety and comfort parameters imposed by design.
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Pande, Sanjay P., and Sarika Khandelwal. "Scene Detection Classification and Tracking for Self-Driven Vehicle." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7s (July 25, 2023): 681–90. http://dx.doi.org/10.17762/ijritcc.v11i7s.7529.
Abstract:
A number of traffic-related issues, including crashes, jams, and pollution, could be resolved by self-driving vehicles (SDVs). Several challenges still need to be overcome, particularly in the areas of precise environmental perception, observed detection, and its classification, to allow the safe navigation of autonomous vehicles (AVs) in crowded urban situations. This article offers a comprehensive examination of the application of deep learning techniques in self-driving cars for scene perception and observed detection. The theoretical foundations of self-driving cars are examined in depth in this research using a deep learning methodology. It explores the current applications of deep learning in this area and provides critical evaluations of their efficacy. This essay begins with an introduction to the ideas of computer vision, deep learning, and self-driving automobiles. It also gives a brief review of artificial general intelligence, highlighting its applicability to the subject at hand. The paper then concentrates on categorising current, robust deep learning libraries and considers their critical contribution to the development of deep learning techniques. The dataset used as label for scene detection for self-driven vehicle. The discussion of several strategies that explicitly handle picture perception issues faced in real-time driving scenarios takes up a sizeable amount of the work. These methods include methods for item detection, recognition, and scene comprehension. In this study, self-driving automobile implementations and tests are critically assessed.
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Donchenko, V. V., and V. A. Kupavtsev. "Study of urban infrastructure elements for personal mobility devices safe movement." Russian Automobile and Highway Industry Journal 20, no. 3 (July 17, 2023): 338–49. http://dx.doi.org/10.26518/2071-7296-2023-20-3-338-349.
Abstract:
Introduction. Despite the introduction of new amendments to the rules of the road, accidents involving personal mobility device (PMD) continue to occur on the roads of the Russian Federation. The analysis of statistical indicators makes possible to determine that more than 90% of accidents occur in populated areas, with the participation of PMDs equipped with electric motor mainly collisions with vehicles, with less powerful, not equipped with electric motor mainly collisions with pedestrians occur, which indicates certain chosen conditions for driving - the roadway and sidewalks. In addition, it was found that in these cases, one of the emerging types of accidents is tipping, associated primarily with the available elements on sections of city streets and city roads, which have a certain height above the level of roads and sidewalks. In order to determine the possibility of a overturning taking these elements into account, the analysis of the urban infrastructure was determined, the main elements that pose a risk to PMD traffic were identified, their geometric parameters were established and a mathematical calculation of the traffic conditions was carried out in the study. Methods and materials. As materials and methods for the study, the methods of statistical analysis and mathematical calculations were defined. Results. As a result of the calculation, the authors identified the elements of the urban infrastructure that are dangerous for the movement of the PMD, when interacting with which, with a high degree of probability, the device in question will overturn and injure the driver of the PMD. Conclusion. The calculations have identified the main hazardous elements located on the city streets and urban roads. It was found that the process of overturning is influenced by the geometric characteristics of the device. In view of the obtained results, the perspective directions of improvement of traffic safety in urban transport systems - development of requirements for safety PMD and creation of specialized infrastructure for the safe movement of the PMD in the conditions of modern cities were determined.
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You, Huimin. "Safe Operation Management of Urban Smart Grid Based on Deep Learning." Mobile Information Systems 2022 (August 24, 2022): 1–11. http://dx.doi.org/10.1155/2022/4184941.
Abstract:
The smart grid in the twenty-first century is constantly innovated by the big data technology and the Internet of Things (IoT) technology. As the second generation of a power network, the smart grid keeps developing towards automation and intelligence, driving the energy conversion rate, power utilization rate, and energy supply rate to increase. However, in the smart grid, the power terminal has a pivotal role in controlling, monitoring, and regulating the production process of electricity, which is currently facing many security challenges. The most critical aspect of smart grid security management is to ensure the security of power terminals. Existing solutions generally monitor power terminal devices by monitoring power terminal traffic; however, such security policies can only monitor attacks with characterization properties at the traffic level and cannot be used to monitor power terminal devices directly. Based on this, this paper reviewed the literature on intelligent operation and maintenance and deep learning at home and abroad and comprehensively analyzed the research progress of intelligent operation and maintenance, and in the comparative analysis of deep learning methods, because the convolutional neural network has fewer connections and parameters and can control the capacity by controlling its depth and width, it is convenient to establish a model with larger learning capacity, so a convolutional neural network is chosen for data analysis. In this study, we choose to use the convolutional neural network to analyze the data, combine the monitoring, management, and fault location of operation and maintenance work organically through some deep learning algorithms, reduce the number of model layers through a deep learning-based security monitoring technology for electric power terminals to improve the training speed and efficiency, and achieve all-round protection for electric power terminals at the device level and the network level. The management of urban smart grid dispatching operation also requires strict implementation of relevant technical standards to ensure the standardized operation and enhance the safety and stability of grid operation.
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Gao, Hao, Yadong Zhang, and Jin Guo. "A Novel Dynamic Programming Approach for Optimizing Driving Strategy of Subway Trains." MATEC Web of Conferences 325 (2020): 01002. http://dx.doi.org/10.1051/matecconf/202032501002.
Abstract:
The reduction of operation energy consumption without decreasing service quality has become a great challenge in subways daily operation. A novel DP based approach is proposed for optimizing the train driving strategy. The optimal driving problem is first considered as a multi-objective problem with five optimal targets (i.e., energy saving, punctual arriving, less switching, safe driving and accurate stopping). The optimization problem is remodelled as a multistage decision problem by discretizing the continuous train movement in space. The process of dynamic programming is carried out in the velocity-space status space. Due to the discretizing rules of searching space, the optimal goals of safe driving and accurate stopping can be satisfied during the searching process. The rest of multiple goals are spilt into cost functions and constrains for each stage. Due to the multiple cost functions, a set of pareto optimal solutions can be achieved at each vertex during the process of dynamic programming. To further improve the efficiency of algorithm, two evaluation criterions are introduced to maintain the capacity of the pareto set at each vertex. A case study of Yizhuang urban rail line in Beijing is conducted to verify the effectiveness and the efficiency of DP based algorithms.
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Zhao, Wei, Jiateng Yin, Xiaohan Wang, Jia Hu, Bozhao Qi, and Troy Runge. "Real-Time Vehicle Motion Detection and Motion Altering for Connected Vehicle: Algorithm Design and Practical Applications." Sensors 19, no. 19 (September 23, 2019): 4108. http://dx.doi.org/10.3390/s19194108.
Abstract:
Real-time capturing of vehicle motion is the foundation of connected vehicles (CV) and safe driving. This study develops a novel vehicle motion detection system (VMDS) that detects lane-change, turning, acceleration, and deceleration using mobile sensors, that is, global positioning system (GPS) and inertial ones in real-time. To capture a large amount of real-time vehicle state data from multiple sensors, we develop a dynamic time warping based algorithm combined with principal component analysis (PCA). Further, the designed algorithm is trained and evaluated on both urban roads and highway using an Android platform. The aim of the algorithm is to alert adjacent drivers, especially distracted drivers, of potential crash risks. Our evaluation results based on driving traces, covering over 4000 miles, conclude that VMDS is able to detect lane-change and turning with an average precision over 76% and speed, acceleration, and brake with an average precision over 91% under the given testing data dataset 1 and 4. Finally, the alerting tests are conducted with a simulator vehicle, estimating the effect of alerting back or front vehicle the surrounding vehicles’ motion. Nearly two seconds are gained for drivers to make a safe operation. As is expected, with the help of VMDS, distracted driving decreases and driving safety improves.
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Dai, Tianhui, and Wanru Huang. "Spacial Choices of Queer Urban Space: A Look into Queer Nightlife Establishments in China." Communications in Humanities Research 31, no. 1 (May 17, 2024): 70–79. http://dx.doi.org/10.54254/2753-7064/31/20231884.
Abstract:
This study examines the dynamics of queer public space in Chengdu, a major Chinese city. Considering the availability of access to different queer public spaces in China, we focus on Chengdu's night venue, entertainment, and digital public space and investigate their significance in creating queer community and sense of belonging. Observation, interviews, case studies, and document review is used to explore the queer space in Chengdu and reveal the obstacles queer community face when constituting safe queer space under the relatively oppressive ideology of the country. Investigating the problems through the lens of multi-disciplines such as urban sociology and gender studies, we take a view of the queer public space itself from an urban planning perspective which examines its design rationality as pure space constitution and analyzes the driving force for such design in gender studies perspective which involves discussion of intersectionality and analysis of patriarchal society. The queer urban space serves a critical function for the queer community by gathering the scattered population and providing a safe space to express their identities. By investigating the contemporary queer public space in Chengdu and making comparisons with that in other countries, this study uncovers the struggle and efforts of the queer community to establish their safe public space in combat with the heteronormative society.
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Boric, Sandra, Edgar Schiebel, Christian Schlögl, Michaela Hildebrandt, Christina Hofer, and Doris M. Macht. "Research in Autonomous Driving – A Historic Bibliometric View of the Research Development in Autonomous Driving." International Journal of Innovation and Economic Development 7, no. 5 (December 2021): 27–44. http://dx.doi.org/10.18775/ijied.1849-7551-7020.2015.74.2003.
Abstract:
Autonomous driving has become an increasingly relevant issue for policymakers, the industry, service providers, infrastructure companies, and science. This study shows how bibliometrics can be used to identify the major technological aspects of an emerging research field such as autonomous driving. We examine the most influential publications and identify research fronts of scientific activities until 2017 based on a bibliometric literature analysis. Using the science mapping approach, publications in the research field of autonomous driving were retrieved from Web of Science and then structured using the bibliometric software BibTechMon by the AIT (Austrian Institute of Technology). At the time of our analysis, we identified four research fronts in the field of autonomous driving: (I) Autonomous Vehicles and Infrastructure, (II) Driver Assistance Systems, (III) Autonomous Mobile Robots, and (IV) IntraFace, i.e., automated facial image analysis. Researchers were working extensively on technologies that support the navigation and collection of data. Our analysis indicates that research was moving towards autonomous navigation and infrastructure in the urban environment. A noticeable number of publications focused on technologies for environment detection in automated vehicles. Still, research pointed at the technological challenges to make automated driving safe.
36
Boric, Sandra, Edgar Schiebel, Christian Schlögl, Michaela Hildebrandt, Christina Hofer, and Doris M. Macht. "Research in Autonomous Driving – A Historic Bibliometric View of the Research Development in Autonomous Driving." International Journal of Innovation and Economic Development 7, no. 5 (December 2021): 27–44. http://dx.doi.org/10.18775/10.18775/ijied.1849-7551-7020.2015.75.2003.
Abstract:
Autonomous driving has become an increasingly relevant issue for policymakers, the industry, service providers, infrastructure companies, and science. This study shows how bibliometrics can be used to identify the major technological aspects of an emerging research field such as autonomous driving. We examine the most influential publications and identify research fronts of scientific activities until 2017 based on a bibliometric literature analysis. Using the science mapping approach, publications in the research field of autonomous driving were retrieved from Web of Science and then structured using the bibliometric software BibTechMon by the AIT (Austrian Institute of Technology). At the time of our analysis, we identified four research fronts in the field of autonomous driving: (I) Autonomous Vehicles and Infrastructure, (II) Driver Assistance Systems, (III) Autonomous Mobile Robots, and (IV) IntraFace, i.e., automated facial image analysis. Researchers were working extensively on technologies that support the navigation and collection of data. Our analysis indicates that research was moving towards autonomous navigation and infrastructure in the urban environment. A noticeable number of publications focused on technologies for environment detection in automated vehicles. Still, research pointed at the technological challenges to make automated driving safe.
37
Jurecki, Rafał S., and Tomasz L. Stańczyk. "A Methodology for Evaluating Driving Styles in Various Road Conditions." Energies 14, no. 12 (June 16, 2021): 3570. http://dx.doi.org/10.3390/en14123570.
Abstract:
For many institutions, it is important to evaluate a given driving technique as safe or unsafe based on measurable vehicle movement parameters. The paper constitutes a part of studies aimed at establishing a method of parameter-based evaluation of drivers in various road conditions, in other words, to create a so-called ‘driver profile’. The tests were carried out on a 650 km route, on four varying road types. Longitudinal and lateral acceleration values are used to evaluate the driving style. An analysis is presented of the impact of the type and shape of road on acceleration values. The results demonstrate that the same driver, when driving the same vehicle on an expressway, an inter-urban road or in urban traffic, will move with various acceleration values. A detailed analysis of acceleration values and distributions was conducted. Interesting conclusions were drawn after excluding the so-called ‘smooth driving’ sections, by acceleration ranges of −0.5 to 0.5 m/s2 from the analysis. This allowed for the evaluation of the structure of other longitudinal and lateral acceleration values. After this modification, the distributions showed specificity for the given road type, thereby allowing the road type used by the vehicle’s driver to be recognized based solely on the distribution.
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Chrisnatalia, Maria, Dian Kemala Putri, Karmilasari, and Stephanus Benedictus Bera Liwun. "The influence of personality type on the risk of driving." INSPIRA: Indonesian Journal of Psychological Research 4, no. 2 (December 20, 2023): 111–22. http://dx.doi.org/10.32505/inspira.v4i2.6916.
Abstract:
Accidents are often caused by the driver himself, other people, or even the circumstances while driving. Even emotions influence behavior while driving. Individual factors relate to personality type. This research aims to determine the influence of agreeableness and neuroticism personality types on driving risks mediated by driving safety attitudes. The methods used in this research are experiments and surveys. The experimental tool used in this research is a driving simulator with the help of Urban Driving software. The experimental group was treated with a traffic volume and pedestrian density of 75%, and the control group was treated with a traffic volume of 50%. The measurement instruments used in this study consisted of a measure of risky driving behavior, a measure of aggressive driving behavior, a measure of agreeableness and neuroticism from the Big Five Scale, and attitudes towards safe driving. Thirty participants in this study were divided into two groups, namely the experimental and the control groups, with 15 participants. Based on the regression results of each group. The regression results show an influence of agreeableness and neuroticism mediated by driving safety attitudes on driving risks in the experimental and control groups; however, both groups have indirect impacts. These results prove that personality type greatly influences driving risk, mediated by driving safety attitudes.
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Liu, Zhuofan, Wei Yuan, and Yong Ma. "Drivers’ Attention Strategies before Eyes-off-Road in Different Traffic Scenarios: Adaptation and Anticipation." International Journal of Environmental Research and Public Health 18, no. 7 (April 2, 2021): 3716. http://dx.doi.org/10.3390/ijerph18073716.
Abstract:
The distribution of drivers’ visual attention prior to diverting focus from the driving task is critical for safety. The object of this study is to investigate drivers’ attention strategy before they occlude their vision for different durations under different driving scenarios. A total of 3 (scenarios) × 3 (durations) within-subjects design was applied. Twenty-three participants completed three durations of occlusion (0, 1, and 2 s) test drive in a motion-based driving simulator under three scenarios (urban, rural, motorway). Drivers’ occlusion behaviour, driving behaviour, and visual behaviour in 6 s before occlusion was analyzed and compared. The results showed that drivers tended to slow down and increased their attention on driving task to keep safety in occlusion 2 s condition. The distribution of attention differed among different driving scenarios and occlusion durations. More attention was directed to Forward position and Speedometer in occlusion conditions, and a strong shift in attention from Forward position to Road users and Speedometer was found in occlusion 2 s condition. Road users was glanced more frequently in urban road with a higher percentage of attention transitions from Forward position to Road users. While gaze switching to Speedometer with a higher intensity was found on motorway. It suggests that drivers could adapt their visual attention to driving demand and anticipate the development of upcoming situations by sampling enough driving-related information before eyes-off-road. Moreover, the adaptation and anticipation are in accordance with driving situation and expected eyes-off-road duration. Better knowledge about attentional strategies before attention away from road contributes to more efficient and safe interaction with additional tasks.
40
Tang, Suhua, Nao Kawanishi, Rei Furukawa, and Nobuaki Kubo. "Experimental Evaluation of Cooperative Relative Positioning for Intelligent Transportation System." International Journal of Navigation and Observation 2014 (November 30, 2014): 1–12. http://dx.doi.org/10.1155/2014/314371.
Abstract:
Support system for safe driving heavily depends on global navigation satellite system. Pseudoranges between satellites and vehicles are measured to compute vehicles’ positions and their relative positions. In urban areas, however, multipath errors (MPEs) in pseudoranges, caused by obstruction and reflection of roadside buildings, greatly degrade the precision of relative positions. On the other hand, simply removing all reflected signals might lead to a shortage of satellites in fixing positions. In our previous work, we suggested solving this dilemma by cooperative relative positioning (CoRelPos) which exploits spatial correlation of MPEs. In this paper, we collected the trace data of pseudoranges by driving cars in urban areas, analyzed the properties of MPEs (specifically, their dependency on signal strength, elevation angles of satellites, and receivers’ speeds), and highlighted their spatial correlation. On this basis, the CoRelPos scheme is refined by considering the dynamics of MPEs. Evaluation results under practical vehicular scenarios confirm that properties of MPEs can be exploited to improve the precision of relative positions.
41
Kang, Jin-Won, Sang-Yub Kim, Joon-Woo Kim, Sol-Bin Lee, and Ki-Chun Nam. "A Correlation Study on Safe Driving Behavior and Cognitive Abilities of Elder Drivers: Focusing on Visuo-spatial Working Memory and Motor Control Abilities." Korean Aging-Frendly Industry Association 15, no. 2 (December 31, 2023): 1–14. http://dx.doi.org/10.34264/jkafa.2023.15.2.1.
Abstract:
Objective : This study aimed to investigate the correlation between subjective and objective evaluations of visuo-spatial working memory and motor control abilities for safe driving among elderly drivers.
Methods : This study evaluated safe driving behavior among Korean elderly drivers aged 60 and above using the Korean safe driving behavior measure (K-SDBM), Spatial Span Test(SST), and Finger Tapping Test(FTT), and conducted a pearson correlation analysis.
Results : The results showed that visuo-spatial working memory span in spatial span test was negatively correlated with the score of the ‘driving situations that require concentration’ factor in the K-SDBM. In addition, the finger tapping test showed a positive correlation between the total number of right hand taps and the ‘general driving skills’ factor in the K-SDBM. This indicates a discrepancy between the subjective cognitive abilities perceived by elder drivers and their objective cognitive abilities.
Conclusion : This study suggests that both subjective driving behavior measures and objective cognitive tests should be used together to assess the safety of elder drivers.
42
Annam, Saikiran, Munavar Fairooz Cheranchery, Archita Chakraborty, and Swati Maitra. "Areas of intervention for enhancing the knowledge of safe driving: An experience in West Bengal, India." Case Studies on Transport Policy 13 (September 2023): 101065. http://dx.doi.org/10.1016/j.cstp.2023.101065.
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Spoerke, Erik D., Howard Passell, Gabriel Cowles, Timothy N. Lambert, Gautam G. Yadav, Jinchao Huang, Sanjoy Banerjee, and Babu Chalamala. "Driving Zn-MnO2 grid-scale batteries: A roadmap to cost-effective energy storage." MRS Energy & Sustainability 9, no. 1 (February 16, 2022): 13–18. http://dx.doi.org/10.1557/s43581-021-00018-4.
Abstract:
Highlights Zn-MnO2 batteries promise safe, reliable energy storage, and this roadmap outlines a combination of manufacturing strategies and technical innovations that could make this goal achievable. Approaches such as improved efficiency of manufacturing and increasing active material utilization will be important to getting costs as low as $100/kWh, but key materials innovations that facilitate the full 2-electron capacity utilization of MnO2, the use of high energy density 3D electrodes, and the promise of a separator-free battery with greater than 2V potential offer a route to batteries at $50/kWh or less. Abstract Large-scale energy storage is certain to play a significant, enabling role in the evolution of the emerging electrical grid. Battery-based storage, while not a dominant form of storage today, has opportunity to expand its utility through safe, reliable, and cost-effective technologies. Here, secondary Zn–MnO2 batteries are highlighted as a promising extension of ubiquitous primary alkaline batteries, offering a safe, environmentally friendly chemistry in a scalable and practical energy dense technology. Importantly, there is a very realistic pathway to also making such batteries cost-effective at price points of $50/kWh or lower. By examining manufacturing examples at the Zn–MnO2 battery manufacturer Urban Electric Power, a roadmap has been created to realize such low-cost systems. By focusing on manufacturing optimization through reduced materials waste, scalable manufacturing, and effective materials selection, costs can be significantly reduced. Ultimately, though, coupling these approaches with emerging research and development advances to enable full capacity active materials utilization and battery voltages greater than 2V are likely needed to drive costs below a target of $50/kWh. Reaching this commercially important goal, especially with a chemistry that is safe, well-known, and reliably effective stands to inject Zn–MnO2 batteries in the storage landscape at a critical time in energy storage development and deployment. Graphical abstract
44
Babisch, Stefan, Christian Neurohr, Lukas Westhofen, Stefan Schoenawa, and Henrik Liers. "Leveraging the GIDAS Database for the Criticality Analysis of Automated Driving Systems." Journal of Advanced Transportation 2023 (May 8, 2023): 1–25. http://dx.doi.org/10.1155/2023/1349269.
Abstract:
A safe introduction of automated driving systems on urban roads requires a thorough understanding of the traffic conflicts and accidents. This understanding is paramount to constructively safeguard these systems, i.e., to design a system that exhibits an adequate performance even in critical situations. In this work, we present an approach to gather knowledge by analyzing the German In-Depth Accident Study (GIDAS) database, which is representative of all German traffic accidents, along with the influencing factors that are hypothesized to be associated with increased criticality in relation to automated driving. In order to gain an insight into the risk associated with these factors in real-world accidents, we determine their presence in the database’s accident cases within a selected operational domain, enabled by translation from a natural language description to the database scheme employed by GIDAS. This initial catalog as well as the subsequent statistical considerations is motivated by analyzing the criticality for automated driving systems in urban areas. Based on this catalog, our work delineates a method for quantification of risk associated with such influencing factors in a given operational domain based on real-world accident data. This quantification can subsequently be used in decompositional, scenario-based risk assessment before system design and for the embedding safety argumentation. This paper, therefore, provides a blueprint of how the matured field of traffic accident research studies and its results, in particular accident databases, can be leveraged for risk assessment of the operational domain of automated driving systems.
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Al Aufi, Ayuob, Cole Schmidt, Jackson Goetz, and Kirolos Haleem. "Investigating “Texting while Driving” Behavior at Different Roadway Configurations Using a Driving Simulator Setting." Transportation Research Record: Journal of the Transportation Research Board 2676, no. 3 (October 21, 2021): 183–200. http://dx.doi.org/10.1177/03611981211049413.
Abstract:
This study investigates the safety impact of distracted driving (texting while driving) for different roadway configurations (intersections, segments, freeways, and roundabouts; urban, suburban, and rural sections; and straight and curved road cross-sections) and various lighting conditions (nighttime and daytime) using a driving simulator. The study took place at Western Kentucky University in Bowling Green, KY. Fifty participants (30 young adults, 18 to 25 years old; 20 middle-/old age adults, 26 to 70 years old) drove the simulator, for approximately 10 min each. Video recordings and behavior observations (e.g., recording single longest off-road eye glance while texting and driving) were further documented. While texting and driving at the roundabout, significant differences were found between the mean lane positions of the young and middle-/old age groups. Additionally, a slightly higher speed variance for middle-/old age drivers existed while texting and driving on freeways during the daytime compared with their younger counterparts. Comparisons with the safe stopping sight distance revealed potential safety risks for all texting while driving situations for both age groups compared with nontexting situations. On average, participants with a higher distracted-driving crash-risk expended 0.676 more seconds glancing off-road than lower distracted-driving crash-risk participants. Furthermore, on average, lower-risk participants had a 3.99 mph speed standard deviation compared with the 5.34 mph speed standard deviation of higher-risk participants. It should be noted that the top five higher-risk drivers were from the middle/older population, whereas the top five lower-risk drivers were from the younger population.
46
Rani, M. F. H., S. Abu Bakar, M. S. M. Hashim, A. Harun, Z. M. Razlan, W. K. Wan, I. Zunaidi, et al. "Calculating the Brake-Application Time of AEB System by Considering Maximum Deceleration Rate during a Primary Accident in Penang's Urban Road." Journal of the Society of Automotive Engineers Malaysia 3, no. 3 (April 29, 2021): 320–32. http://dx.doi.org/10.56381/jsaem.v3i3.130.
Abstract:
An experimental study of the brake-application time of Autonomous Emergency Braking (AEB) system considering the primary accident in an urban area was proposed. Since the functionality of the brake-application time is varied between manufacturers and models, the brake-application time of AEB system must be verified based on driving behaviour in Malaysia. A primary accident was simulated to acquire vehicle deceleration rate in real condition by driving an ego vehicle at a different set of vehicle speeds. The study is focussed on the urban roads in the north region of West Malaysia, i.e. Penang. As a benchmark in this study, the brake-application time (2.6 s) introduced by Mercedes-Benz in the PRE-SAFE® Brakes technology was referred. A new braking permission time was proposed by calculating a minimum deceleration distance and Time-to-Collison (TTC) confirmation time required to brake based on maximum deceleration when a primary accident was simulated. It was found that the brake-application time recommended for the AEB system, specifically AEB City conveys the real driving condition of Penang when a primary accident happens in the urban area. To have a smooth braking and an optimum braking performance during a primary accident, the Forward Collision Warning (FCW) should be activated at TTC ≤ 4.6 s. The partial braking (PB) should be activated automatically when the TTC is approximately 2.9 s. While the automated full braking (FB) phase should begin when the TTC reaches 1.1 s.
47
Rock, Teresa, Taras Hryhoruk, Thomas Bleher, Mohammad Bahram, Stefanie Marker, Arslan Ali Syed, and Maya Sekeran. "Objectively Scoring the Human-Likeness of Artificial Driver Models." Applied Sciences 13, no. 18 (September 11, 2023): 10218. http://dx.doi.org/10.3390/app131810218.
Abstract:
Several applications of artificially modeled drivers, such as autonomous vehicles (AVs) or surrounding traffic in driving simulations, aim to provide not only functional but also human-like behavior. The development of human-like AVs is expected to improve the interaction between AVs and humans in traffic, whereas, in a driving simulation, the objective is to create realistic replicas of real driving scenarios to investigate various research questions under safe and reproducible conditions. In urban traffic, driving behavior strongly depends on the situational context, which introduces new challenges not only for modeling but also for the evaluation of such models. However, current objective assessment strategies rarely consider situational context and human similarity, whereas subjective approaches are not suitable for iterative development processes. In this paper, we present a first attempt to make the plausibility and human-likeness of vehicles’ trajectories objectively measurable. A multidimensional quality function is presented that incorporates various parameters characterizing human-like driving behavior and compares each of those parameters to human driving behavior under similar conditions. Among other things, our validation results show that the presented evaluation methodology is scalable to a wide range of situations has the ability to identify model weaknesses, and is able to reflect the way people distinguish between artificial and human behavior.
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Zhang, Senlin, Guohong Deng, Echuan Yang, and Jian Ou. "Optimal Vehicle Lane Change Trajectory Planning in Multi-Vehicle Traffic Environments." Applied Sciences 12, no. 19 (September 26, 2022): 9662. http://dx.doi.org/10.3390/app12199662.
Abstract:
Autonomous driving technology in urban environments is a very important avenue of research. Notably, the question of how to plan safe lane-changing trajectories is a challenge in multi-vehicle traffic environments. In our research, three kinds of polynomial lane changing mathematical models were analyzed and compared. It was found that the fifth polynomial is the most suitable for lane changing trajectories; it is defined as a generalized lane-changing trajectory cluster, whereby the minimum lane change time is determined by the vehicle lateral stability threshold. Here, a collision avoidance algorithm is proposed to eliminate unsafe trajectories. Finally, the TOPSIS algorithm is used to solve the multi-objective optimization problem, and the optimal lane-changing expected trajectory is obtained from the safe trajectory cluster. The simulation results showed improvements in lane-changing efficiency of 6.67% and no collisions in the overtaking condition. In general, the proposed method of identifying the optimal lane changing trajectory can achieve safe, efficient and stable lane changing.
49
Astrov, Igor. "A Model-Based Control of Self-Driving Car Trajectory for Lanes Change Maneuver in a Smart City." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 18 (October 27, 2023): 346–53. http://dx.doi.org/10.37394/23203.2023.18.36.
Abstract:
High-quality computer control of autonomous vehicles in various environments is a priority for cyber-physical systems (CPS), Industry 4.0, and the global economy as a whole. The paper discusses the linearized control model of a Self-Driving Car (SDC) with a weight of 1160 kg. For safe maneuvering with obstacle avoidance, we employ an optimal control by Linear Quadratic Regulator (LQR) using a Simulink/MATLAB environment that is capable to demonstrate the satisfiability of LQR control for this maneuver using a 3D simulation environment under changing urban conditions in a smart city. This controller is easy for engineering implementation.
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Vo, Cong Phat, and Jeong hwan Jeon. "An Integrated Motion Planning Scheme for Safe Autonomous Vehicles in Highly Dynamic Environments." Electronics 12, no. 7 (March 26, 2023): 1566. http://dx.doi.org/10.3390/electronics12071566.
Abstract:
This study proposes a new integrated approach to the motion control of autonomous vehicles, which differs from the conventional method of treating planning and tracking tasks as separate or hierarchical components. By means of the proposed approach we can reduce the side effects on the performance of autonomous vehicles under challenging driving circumstances. To this end, our approach processes both of the aforementioned tasks asynchronously and simultaneously utilizes a multi-threaded architecture to enhance control performance. Meanwhile, the behavior planning feature is integrated into the path-tracking module. Then, a linear parameter-varying model predictive control is deployed for trajectory tracking of autonomous vehicles and compared with the linear model predictive control method. Finally, the control performance of the proposed approach was evaluated through simulation trials on urban roads with placed obstacles. The outcomes revealed that the suggested framework satisfies the processing rate and high-precision criteria, while safely avoiding obstacles, indicating that it is a promising control strategy for real-world applications.