Relevant bibliographies by topics / Intelligent vehicles

Academic literature on the topic 'Intelligent vehicles'

Author: Grafiati
Published: 4 June 2021
Last updated: 24 August 2024

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Journal articles on the topic "Intelligent vehicles"

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Wang, Biyao, Yi Han, Di Tian, and Tian Guan. "Sensor-Based Environmental Perception Technology for Intelligent Vehicles." Journal of Sensors 2021 (September 2, 2021): 1–14. http://dx.doi.org/10.1155/2021/8199361.

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Environmental perception technology is the basis and premise of intelligent vehicle decision control of intelligent vehicles, a crucial link of intelligent vehicles to realize intelligence, and also the basic guarantee of its safety and intelligence. The accuracy and robustness of the perception algorithm will directly affect or even determine the realization of the upper function of intelligent vehicles. The wrong environmental perception will affect the control of the vehicle, thus causing safety risks. This paper discusses the intelligent vehicle perception technology and introduces the development status and control strategies of several important sensors such as machine vision, laser radar, and millimeter-wave radar. Target detection, target recognition, and multisensor fusion are analyzed in the optimized part of sensor results. The functions of the intelligent vehicle assistance system which has been applied to the ground at present are described, and the lane detection, adaptive cruise control (ACC), and autonomous emergency braking (AEB) are analyzed. Finally, the paper looks forward to the research direction of sense-based intelligent vehicle perception technology, which will play an important role in guiding the development of intelligent vehicles and accelerate the landing process of intelligent vehicles.
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Xiong, Xiaoxia, Shiya Zhang, and Yuexia Chen. "Review of Intelligent Vehicle Driving Risk Assessment in Multi-Vehicle Interaction Scenarios." World Electric Vehicle Journal 14, no. 12 (December 14, 2023): 348. http://dx.doi.org/10.3390/wevj14120348.

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With the rapid breakthroughs in artificial intelligence technology and intelligent manufacturing technology, automotive intelligence has become a research hotspot, and much progress has been made. However, a skeptical attitude is still held towards intelligent vehicles, especially when driving in a complex multi-vehicle interaction environment. The interaction among multi-vehicles generally involves more uncertainties in vehicle motion and entails higher driving risk, and thus deserves more research concerns and efforts. Targeting the safety assessment issue of complex multi-vehicle interaction scenarios, this article summarizes the existing literature on the relevant data collection methodologies, vehicle interaction mechanisms, and driving risk evaluation methods for intelligent vehicles. The limitations of the existing assessment methods and the prospects for their future development are analyzed. The results of this article can provide a reference for intelligent vehicles in terms of timely and accurate driving risk assessment in real-world multi-vehicle scenarios and help improve the safe driving technologies of intelligent vehicles.
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Seth, Ishita, Kalpna Guleria, and Surya Narayan Panda. "Introducing Intelligence in Vehicular Ad Hoc Networks Using Machine Learning Algorithms." ECS Transactions 107, no. 1 (April 24, 2022): 8395–406. http://dx.doi.org/10.1149/10701.8395ecst.

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The automotive industry has gained popularity in the past decade, leading to tremendous advancements in intelligent vehicular networks. The increase in the number of vehicles on the roads makes it essential for vehicles to act intelligently as humans do. The concept of machine learning is that when vehicles learn and improve to operate by the previously processed data. The machine learning techniques have helped the automotive industry develop the driverless car. With the help of sensors and cameras, it is quite possible to use the machine learning algorithms and provide the user with its benefits. It helps to allow the vehicle to perform specific tasks that actually can replace the vehicle's driver. The Artificial Intelligence (AI) chips integrated into the vehicles enable the vehicle to navigate roads. This paper provides insight into the machine learning algorithms widely used by the automotive industries, and a comparison is made between them concerning the Vehicular Ad Hoc Network (VANET) applications.
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Zhu, Guangyu, Fuquan Zhao, Haokun Song, and Zongwei Liu. "Cost Analysis of Vehicle-Road Cooperative Intelligence Solutions for High-Level Autonomous Driving: A Beijing Case Study." Journal of Advanced Transportation 2024 (January 23, 2024): 1–22. http://dx.doi.org/10.1155/2024/6170743.

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The development of the vehicle-road cooperative intelligence can effectively resolve the current technical impediment and cost quandary associated with high-level autonomous driving. Nevertheless, the intelligent infrastructure entails initial deployment costs and ongoing energy consumption and maintenance costs, necessitating a comprehensive and quantitative analysis of the costs of intelligent infrastructure and the corresponding changes in comprehensive costs. The cost evaluation model for the cooperative intelligent system is designed in this paper, considering the corresponding intelligent infrastructure layout scheme for different road types within the technical framework. The intelligent configuration and corresponding cost transfer from roadside to vehicle side under the synergy effect is also analyzed. Using Beijing as a case study, the results indicate that the deployment of intelligent infrastructure will effectively reduce acquisition and usage costs of high-level intelligent vehicles and achieve a greater “reuse” effect by serving more intelligent connected vehicles (ICVs). Compared to the vehicle intelligence, collaborative intelligence will reduce cumulative total costs by more than ¥200 billion from 2023 to 2050, even with the inclusion of intelligent infrastructure’s costs.
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Su, Xiaozhi, Fangrong Chen, Bowei Li, Liangchen Liu, and Yun Xiang. "Analysis of Carbon Emissions in Heterogeneous Traffic Flow within the Influence Area of Highway Off-Ramps." Applied Sciences 13, no. 17 (August 23, 2023): 9554. http://dx.doi.org/10.3390/app13179554.

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With the continuous advancements in electrification, connectivity, and intelligence in the automotive industry, the mixed traffic of vehicles with different levels of driving automation is changing the carbon emission characteristics in the impact areas of off-ramps on highways. Considering the insufficient research on the carbon emission characteristics of heterogeneous traffic flow in the downstream influence areas of highway off-ramps, this study applied a scenario analysis method. Furthermore, considering factors such as vehicle composition, road control, and platoon management, it establishes and calibrates measurement models for carbon emissions from conventional vehicles, intelligent vehicles, the platoon driving of electric vehicles, and the mixed platoon driving of conventional vehicles and electric vehicles. This study also provides a simulation scenario for a three-lane highway off-ramp based on the actual conditions of the Xi’an Ring Expressway. Finally, by applying the constructed carbon emission calculation models for heterogeneous traffic flow in the intelligent vehicle mixed traffic scenario, a quantitative analysis was conducted to assess the impacts of the intelligent vehicle infiltration rate, off-ramp vehicle proportion, smart-vehicle-dedicated lanes, and platoon driving on carbon emissions in the downstream influence area of off-ramps. The results revealed the impact of intelligent vehicle integration and platoon driving on carbon emission characteristics in the downstream influence areas of highway off-ramps.
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Gao, Fei, Xiaojun Ge, Jinyu Li, Yuze Fan, Yun Li, and Rui Zhao. "Intelligent Cockpits for Connected Vehicles: Taxonomy, Architecture, Interaction Technologies, and Future Directions." Sensors 24, no. 16 (August 10, 2024): 5172. http://dx.doi.org/10.3390/s24165172.

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Highly integrated information sharing among people, vehicles, roads, and cloud systems, along with the rapid development of autonomous driving technologies, has spurred the evolution of automobiles from simple “transportation tools” to interconnected “intelligent systems”. The intelligent cockpit is a comprehensive application space for various new technologies in intelligent vehicles, encompassing the domains of driving control, riding comfort, and infotainment. It provides drivers and passengers with safety, comfort, and pleasant driving experiences, serving as the gateway for traditional automobile manufacturing to upgrade towards an intelligent automotive industry ecosystem. This is the optimal convergence point for the intelligence, connectivity, electrification, and sharing of automobiles. Currently, the form, functions, and interaction methods of the intelligent cockpit are gradually changing, transitioning from the traditional “human adapts to the vehicle” viewpoint to the “vehicle adapts to human”, and evolving towards a future of natural interactive services where “humans and vehicles mutually adapt”. This article reviews the definitions, intelligence levels, functional domains, and technical frameworks of intelligent automotive cockpits. Additionally, combining the core mechanisms of human–machine interactions in intelligent cockpits, this article proposes an intelligent-cockpit human–machine interaction process and summarizes the current state of key technologies in intelligent-cockpit human–machine interactions. Lastly, this article analyzes the current challenges faced in the field of intelligent cockpits and forecasts future trends in intelligent cockpit technologies.
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Mishra, Risabh, M. Safa, and Aditya Anand. "Internet of Vehicles: Commencing Intellectual Hoarse Towards Self-Regulating Cars and Vehicular Clouds for Smart Transportation Structure [Vehicular Ad-Hoc Network: A Review and Application in the Internet of Vehicles]." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 545. http://dx.doi.org/10.14419/ijet.v7i3.12.16176.

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Recent advances in wireless communication technologies and automobile industry have triggered a significant research interest in the field of Internet of Vehicles over the past few years.The advanced period of the Internet of Things is guiding the development of conventional Vehicular Networks to the Internet of Vehicles.In the days of Internet connectivity there is need to be in safe and problem-free environment.The Internet of Vehicles (IoV) is normally a mixing of three networks: an inter-vehicleNetwork, an intra-vehicle network, and a vehicle to vehicle network.Based on idea of three networks combining into one, we define Internet of Vehicles as a large-scale distributed system to wireless communication and information exchange between vehicle2X (X: vehicle, road, human and internet).It is a combined network for supporting intelligent traffic management, intelligent dynamic information service, and intelligent vehicle control, representation of an application of the Internet of Things (IoT) technology for intelligent transportation system (ITS).
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Zhang, Linan, Yizhe Wang, and Huaizhong Zhu. "Theory and Experiment of Cooperative Control at Multi-Intersections in Intelligent Connected Vehicle Environment: Review and Perspectives." Sustainability 14, no. 3 (January 28, 2022): 1542. http://dx.doi.org/10.3390/su14031542.

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A heterogeneous traffic flow consists of regular vehicles, and intelligent connected vehicles having interactive functions is updating the composition of the current urban-road network traffic flow. It has been a growing trend and will continue to be so. Because of the urgent demand, the research focused on three main parts of cooperative control methods under intelligent connected vehicles environment, typical traffic control application scenarios and experimental validation in intelligent connected vehicles conditions, and intersection-oriented hybrid traffic control mechanism for urban road. For heterogeneous interrupted traffic flow of intelligent connected vehicles, to analyze the characteristics and information extraction method of heterogeneous traffic flow of intelligent connected vehicles under different conditions, the research examined driving modes of regular vehicles and intelligent connected vehicles, including car following and lane changing. This study summarized control modes of traffic-signal control, active control of intelligent connected vehicles, and indirect control of regular vehicles through intelligent vehicles to study the active control mechanism and multi-intersection coordinated control strategy for intelligent connected vehicle heterogeneous traffic flow. With the combination of coordinated control theory, this work overviewed integrated experiment of information interaction and coordinated control under intelligent-connected-vehicle heterogeneous traffic-flow environments.
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Ming, Guo. "Exploration of the intelligent control system of autonomous vehicles based on edge computing." PLOS ONE 18, no. 2 (February 2, 2023): e0281294. http://dx.doi.org/10.1371/journal.pone.0281294.

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The development of science and technology continues to promote the progress of society. The current intelligence and automation technology has become widely used in society. To this end, this study proposes a vehicle intelligent control system based on edge computing and deep learning to promote the far-reaching development of intelligent technology and automation technology. First, control algorithms are used to design a switch control strategy combining accelerator and brake. Second, a fuzzy control algorithm based on vehicle tracking and trajectory deviation is designed to enhance the vehicle’s stability during steering. A Convolutional Neural Network (CNN) is used to recognize the car’s surroundings as it drives. In addition, accelerator and brake controllers and vehicle tracking and trajectory deviation controllers are connected to the vehicle’s wiring. Then, the data transmission function based on edge computing is applied to the vehicle’s intelligent control system. Finally, trajectory tracking and emergency braking experiments are carried out on the control system to verify the practicability and reliability of the method and the effectiveness of CNN. The simulation experiments are carried out on two states of medium speed and high speed to verify the effectiveness of the longitudinal anti-collision system of the test vehicle when the target vehicle suddenly decelerates. The results demonstrate that the driving speed of the experimental vehicle is set to 50km/h, the distance between the experimental vehicle and the target vehicle is 40m, and the target vehicle in front drives at a constant speed of 50km/h. The target vehicle in front of the car suddenly decelerates in 5 seconds, and the speed drops to 0 after 5 seconds. The actual distance between the experimental vehicle and the target vehicle is very close to the expected safe space, and the experimental vehicle is in a safe state during this process. When the experimental vehicle starts to decelerate, the experimental vehicle adopts emergency deceleration to ensure a safe distance between the two vehicles. At this time, the car enters the second-level early warning state, but driving safety can still be guaranteed. It is advisable to maintain low-speed emergency braking in this state. This study provides creative research ideas for the follow-up research on the intelligent control system of uncrewed vehicles and contributes to the development of intelligence and automation technology.
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Do, Wooseok, Omid M. Rouhani, and Luis Miranda-Moreno. "Simulation-Based Connected and Automated Vehicle Models on Highway Sections: A Literature Review." Journal of Advanced Transportation 2019 (June 26, 2019): 1–14. http://dx.doi.org/10.1155/2019/9343705.

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This study provides a literature review of the simulation-based connected and automated intelligent-vehicle studies. Media and car-manufacturing companies predict that connected and automated vehicles (CAVs) would be available in the near future. However, society and transportation systems might not be completely ready for their implementation in various aspects, e.g., public acceptance, technology, infrastructure, and/or policy. Since the empirical field data for CAVs are not available at present, many researchers develop micro or macro simulation models to evaluate the CAV impacts. This study classifies the most commonly used intelligent-vehicle types into four categories (i.e., adaptive cruise control, ACC; cooperative adaptive cruise control, CACC; automated vehicle, AV; CAV) and summarizes the intelligent-vehicle car-following models (i.e., Intelligent Driver Model, IDM; MICroscopic Model for Simulation of Intelligent Cruise Control, MIXIC). The review results offer new insights for future intelligent-vehicle analyses: (i) the increase in the market-penetration rate of intelligent vehicles has a significant impact on traffic flow conditions; (ii) without vehicle connections, such as the ACC vehicles, the roadway-capacity increase would be marginal; (iii) none of the parameters in the AV or CAV models is calibrated by the actual field data; (iv) both longitudinal and lateral movements of intelligent vehicles can reduce energy consumption and environmental costs compared to human-driven vehicles; (v) research gap exists in studying the car-following models for newly developed intelligent vehicles; and (vi) the estimated impacts are not converted into a unified metric (i.e., welfare economic impact on users or society) which is essential to evaluate intelligent vehicles from an overall societal perspective.
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Dissertations / Theses on the topic "Intelligent vehicles"

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Aycard, Olivier. "Contribution to Perception for Intelligent Vehicles." Habilitation à diriger des recherches, Université de Grenoble, 2010. http://tel.archives-ouvertes.fr/tel-00545774.

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Perceiving or understanding the environment surrounding of a vehicle is a very important step in building driving assistant systems or autonomous vehicles. In this thesis, we focus on using laser scanner as a main perception sensor in context of dynamic outdoor environments. To solve this problem, we have to deal with 3 main tasks: (1) identify static part and dynamic entities moving in the environment, (2) use static part of the environment to build a map of the environment and localize the vehicle inside this map: this task is know as "Simultaneous Localization And Mapping" (SLAM) and finally (3) Detect And Track Moving Objects (DATMO). Regarding SLAM, the first contribution of this research is made by a grid-based approach\footnote{An occupancy grid is a decomposition of the environment in rectangular cells where each cell contains the probability that it is occupied by an obstacle.} to solve both problems of SLAM and detection of moving objects. To correct vehicle location from odometry we introduce a new fast incremental scan matching method that works reliably in dynamic outdoor environments. After good vehicle location is estimated, the surrounding map is updated incrementally and moving objects are detected without a priori knowledge of the targets. Our second contribution is an efficient, precise and multiscale representation of 2D/3D environment. This representation is actually an extension of occupancy grid where (1) only cells corresponding to occupied part of the environment are stored and updated (2) where cells are represented by a cluster of gaussian to have a fine representation of the environment and (3) where several occupancy grids are used to store and update a multiscale representation of the environment. Regarding DATMO, we firstly present a method of simultaneous detection, classification and tracking moving objects. A model-based approach is introduced to interpret the laser measurement sequence over a sliding window of time by hypotheses of moving object trajectories. The data-driven Markov chain Monte Carlo (DDMCMC) technique is used to explore the hypothesis space and effectively find the most likely solution. An other important problem to solve regarding DATMO is the definition of an appropriate dynamic model. In practice, objects can change their dynamic behaviors over time (e.g. : stopped, moving, accelerating, etc...). To adapt to these changing behaviors, a multiple dynamic model is generally required. But, this set of dynamic models and interactions between these models are always given a priori. Our second contribution on DATMO is a method to guide in the choice of motion models and in the estimation of interactions between these motion models. The last part of this thesis reports integration of these contributions on different experimental platforms in the framework of some national and european projects. Evaluations are presented which confirm the robustness and reliability of our contributions.
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Yamane, Hayato, Kazuya Tanaka, Nobutaka Kito, Daisuke Yamamoto, and Katashi Nagao. "Attentive Townvehicle : Communicating Personal Intelligent Vehicles." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2004. http://hdl.handle.net/2237/10353.

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Sörme, Jacob. "Intelligent Charging Algorithm for Electric Vehicles." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280808.

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Electric vehicles play an important role in creating a fossil free transport sector. Making the vehicles efficient involves many new areas outside the manufacturing process, such as chargers, power grids and electricity markets. This thesis models the charging of electric vehicles using a Markov Decision Process and uses Reinforcement Learning solution models to derive an intelligent charging algorithm. This algorithm can take concepts such as electricity price, battery degradation and electrical losses into account in order to minimise the overall operational costs, and add more value to the use of electric vehicles. Models of how voltage varies in a battery is used and data on causes of battery degradation are derived from modern papers within battery technology. The intelligent charging algorithm is compared to baseline charging algorithms, one of which correspond to how charging is regularly performed today. Vehicle-to Grid is a promising future technology where electric vehicles can discharge some of their energy back to the grid in order to alleviate the stress of a power grid constrained by increasing demand as well an increasing penetration of intermittent sustainable sources of electricity such as wind and solar. Simulations are performed over scenarios with different electricity prices and the implications of being able to utilise Vehicle-to-Grid is studied. Results from simulations show that the intelligent charging algorithm effectively can reduce costs by approximately 30% on average compared to regular charging when the charging sessions last for 7 hours. Vehicle-to-Grid was seen to only be able to reduce costs in simulations with inexpensive batteries on days when there was a large difference in electricity price. The intelligent charging was able to save as much as 500 SEK for long charging sessions with expensive batteries, and powerful chargers. Results show a promising future for an intelligent charging algorithm to be used in order to improve the efficiency of electric vehicle charging.
Elektriska fordon spelar en viktig roll för målet att skapa en tranportindustri som inte förlitar sig på fossila bränslen. Utmaningen att göra elektriska fordon så effektiva som möjligt innefattar många nya områden som ligger utanför det faktiska tillverkandet, som laddinfrastruktur, elnät och marknader för elektricitetshandel. Detta examensarbete modellerar laddning av elektriska fordon med Markov-beslutsprocesser och använder algoritmer från förstärkt inlärning för att ta fram en intelligent laddalgoritm. Denna algoritm kan ta indata från koncept som elpris och batteridegradering samt räkna med elektriska förluster, allt för att minska driftkostnad och göra det mer värdefullt att använda elfordon. Modeller för hur spänning varierar används och data för hur batterier degraderas används från moderna rapporter inom batteriteknologi. Den intelligenta laddalgoritmen jämförs med andra tillvägagångssätt att ladda, bland annat ett som motsvarar hur laddning ofta utförs idag. Vehicle-to-Grid är en lovande framtida teknologi som innebär att elektriska fordon kan ladda ur energi ur sina batterier och sälja tillbaka till elnätet för att reducera belastningar i nätet, dels på grund av ökad efterfrågan men också på grund av att elnätet i framtiden kan bestå av mindre pålitliga men förnyelsebara energikällor som solceller och vindkraft. Simuleringar körs över situationer med olika elpris och effekterna av att kunna använda Vehicle-to-Grid studeras. Resultat visar på att intelligent laddning kan spara ungefär 30% av kostnaderna i snitt. Simuleringarna visar att Vehicle-to-Grid endast kan spara kostnader då batterierna är billiga och då elpriset uppvisar stora variationer. Den intelligenta laddningsalgoritmen kun de spara upp till 500 SEK vid laddsessioner som varade en lång tid, med dyra batterier och med kraftfulla laddare. Resultaten visar på en lovande framtid för intelligenta laddalgorimer att användas för att öka effektiviteten inom laddning av elektriska fordon.
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Graham, James. "Intelligent power management for unmanned vehicles." Thesis, Loughborough University, 2015. https://dspace.lboro.ac.uk/2134/18026.

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Unmanned Air Vehicles (UAVs) are becoming more widely used in both military and civilian applications. Some of the largest UAVs have power systems equivalent to that of a military strike jet making power management an important aspect of their design. As they have developed, the amount of power needed for loads has increased. This has placed increase strain on the on-board generators and a need for higher reliability. In normal operation these generators are sized to be able to power all on-board systems with out overheating. Under abnormal operating conditions these generators may start to overheat, causing the loss of the generator's power output. The research presented here aims to answer two main questions: 1) Is it possible to predict when an overheat fault will occur based on the expected power usage defined by mission profiles? 2) Can an overheat fault be prevented while still allowing power to be distributed to necessary loads to allow mission completion? This is achieved by a load management algorithm, which adjusts the load profile for a mission, by either displacing the load to spare generators, or resting the generator to cool it down. The result is that for non-catastrophic faults the faulty generator does not need to be fully shut down and missions can continue rather than having to be aborted. This thesis presents the development of the load management system including the algorithm, prediction method and the models used for prediction. Ultimately, the algorithms developed are tested on a generator test rig. The main contribution of this work is the design of a prognostic load management algorithm. Secondary contributions are the use of a lumped parameter thermal model within a condition monitoring application, and the creation of a system identification model to describe the thermal dynamics of a generator.
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Li, Li Wang Fei-Yue. "Advanced motion control and sensing for intelligent vehicles." New York : Springer, 2007. http://www.myilibrary.com?id=113830.

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Nagao, Katashi, Kazutoshi Kozakai, Meguru Ito, Issei Naruta, and Shigeki Ohira. "Attentive Townvehicle Environment-Aware Personal Intelligent Vehicles." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2005. http://hdl.handle.net/2237/10371.

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Stephens, Michael. "Intelligent adaptive control of remotely operated vehicles." Thesis, University of Liverpool, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.240889.

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Chamitoff, Gregory Errol. "Robust intelligent flight control for hypersonic vehicles." Thesis, Massachusetts Institute of Technology, 1992. http://hdl.handle.net/1721.1/44275.

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Glenn, Bradley C. "Intelligent Control of Parallel Hybrid Electric Vehicles." The Ohio State University, 1999. http://rave.ohiolink.edu/etdc/view?acc_num=osu1391600950.

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Ozgunalp, Umar. "Vision based lane detection for intelligent vehicles." Thesis, University of Bristol, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.691261.

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Most vehicle accidents are due to driver error or slow reaction time. To prevent or minimize the consequences of these accidents, Advanced Driver Assistance Systems (ADAS) are introduced and lane detection is one of the most important building blocks of ADAS. Thus, the main focus of this thesis is lane detection. In this thesis, initially, lane detection algorithms based on a single camera as a sensor are investigated and proposed. First, an Inverse Perspective Mapping (IPM) based lane detection algorithm is proposed, where the global lane orientation and lane connectivity in this direction is exploited to increase the Signal to Noise Ratio (SNR). Using the initially estimated lane orientation, feature map is iteratively shifted and matched with itself to eliminate noise. Furthermore, based on the global lane orientation, an accurate, and linear Region of Interest (ROI) is efficiently formed using a I-D likelihood accumulator, where lane pairs are fitted to the feature points in estimated ROI. Later, an extension to the Symmetrical Local Threshold (SLT) is proposed for more accurate feature map extraction. Despite low computational complexity of the SLT, the algorithm outperformed all of the tested lane feature extractors in the Road Marking (ROMA) data sets. However, the main drawback of this algorithm is it cannot supply orientation information for the feature points. The proposed extension to the SLT, both reduced the noise (tested using ROMA data sets) , and outputs orientation information for the extracted feature points. Then, the extracted feature map and feature point orientations, are exploited for an efficient lane detection, where lane categorization is achieved by using a mask in the Hough domain. Although, single camera can be used for lane detection, single camera cannot supply depth information. Thus, many lane detection algorithms using single camera input are based on assumptions such as flat road assumption. However, 3D input can be utilized for lane detection application on non-flat roads.
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Books on the topic "Intelligent vehicles"

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Cheng, Hong. Autonomous Intelligent Vehicles. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-4471-2280-7.

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Eskandarian, Azim. Handbook of Intelligent Vehicles. London: Springer London, 2012.

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Hebert, Martial H., Charles Thorpe, and Anthony Stentz, eds. Intelligent Unmanned Ground Vehicles. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6325-9.

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Engineers, Society of Automotive. Intelligent vehicles & transportation systems. Warrendale, PA: SAE International, 2006.

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Engineers, Society of Automotive, and Intelligent Vehicle Initiative (U.S.). Technology, eds. Intelligent vehicles & transportation systems. Warrendale, PA: Society of Automotive Engineers, 2006.

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Eskandarian, Azim. Handbook of intelligent vehicles. London: Springer, 2012.

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Eskandarian, Azim. Handbook of intelligent vehicles. London: Springer, 2012.

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Han, Qinglong, Sean McLoone, Chen Peng, and Baolin Zhang, eds. Intelligent Equipment, Robots, and Vehicles. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7213-2.

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Olaverri-Monreal, Cristina, Rosaldo J. F. Rossetti, and Fernando García-Fernández. Human Factors in Intelligent Vehicles. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338475.

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Yu, Huafeng, Xin Li, Richard M. Murray, S. Ramesh, and Claire J. Tomlin, eds. Safe, Autonomous and Intelligent Vehicles. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-97301-2.

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Book chapters on the topic "Intelligent vehicles"

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Broggi, Alberto, Alexander Zelinsky, Michel Parent, and Charles E. Thorpe. "Intelligent Vehicles." In Springer Handbook of Robotics, 1175–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-30301-5_52.

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Broggi, Alberto, Alex Zelinsky, Ümit Özgüner, and Christian Laugier. "Intelligent Vehicles." In Springer Handbook of Robotics, 1627–56. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32552-1_62.

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Wang, Fei-Yue. "Intelligent Vehicles intelligent vehicle (IV) Technology intelligent vehicle (IV) technologies , Introduction." In Encyclopedia of Sustainability Science and Technology, 5460–61. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-0851-3_928.

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Wang, Fei-Yue. "Intelligent Vehicles intelligent vehicle (IV) Technology intelligent vehicle (IV) technologies , Introduction." In Transportation Technologies for Sustainability, 627–28. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-5844-9_928.

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Wu, Wufei, Ryo Kurachi, Gang Zeng, Yuhao Wang, Hiroaki Takada, and Keqin Li. "Intelligent Connected Vehicles." In Cybersecurity and High-Performance Computing Environments, 285–308. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003155799-10.

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Cheng, Hong. "The Framework of Intelligent Vehicles." In Autonomous Intelligent Vehicles, 23–29. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-4471-2280-7_3.

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Cheng, Hong. "Longitudinal Motion Control for Intelligent Vehicles." In Autonomous Intelligent Vehicles, 139–50. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-4471-2280-7_10.

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Eskandarian, Azim. "Introduction to Intelligent Vehicles." In Handbook of Intelligent Vehicles, 1–13. London: Springer London, 2012. http://dx.doi.org/10.1007/978-0-85729-085-4_1.

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Wilson, Christopher. "Probes and Intelligent Vehicles." In Handbook of Intelligent Vehicles, 1145–72. London: Springer London, 2012. http://dx.doi.org/10.1007/978-0-85729-085-4_47.

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Cheng, Hong. "Introduction." In Autonomous Intelligent Vehicles, 3–11. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-4471-2280-7_1.

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Conference papers on the topic "Intelligent vehicles"

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Yan, Danshu, Zhiguo Zhao, Kaichong Liang, and Qin Yu. "Cooperative Lane Change Control Based on Null-Space-Behavior for a Dual-Column Intelligent Vehicle Platoon." In SAE 2023 Intelligent and Connected Vehicles Symposium. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-01-7064.

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<div class="section abstract"><div class="htmlview paragraph">With the extension of intelligent vehicles from individual intelligence to group intelligence, intelligent vehicle platoons on intercity highways are important for saving transportation costs, improving transportation efficiency and road utilization, ensuring traffic safety, and utilizing local traffic intelligence [<span class="xref">1</span>]. However, there are several problems associated with vehicle platoons including complicated vehicle driving conditions in or between platoon columns, a high degree of mutual influence, dynamic optimization of the platoon, and difficulty in the cooperative control of lane change. Aiming at the dual-column intelligent vehicle platoon control (where “dual-column” refers to the vehicle platoon driving mode formed by multiple vehicles traveling in parallel on two adjacent lanes), a multi-agent model as well as a cooperative control method for lane change based on null space behavior (NSB) for unmanned platoon vehicles are established in this paper. Specifically, a multi-agent model of the dual-column vehicle platoon is first established, which adopted a dual-star communication architecture based on “vehicle-to-vehicle” interactions. Then, rules for changing lanes between platoons are designed, and a method based on the risk perception coefficient for determining the priority of the task is developed. Finally, a cooperative control method of lane change based on NSB is proposed to further resolve the conflict between the lane change task and the collision avoidance task. The cooperative control method based on NSB is validated under the condition of sudden deceleration during the lane change task using a driving simulator. Validation results demonstrate that the method can ensure the safety of the platoon and implement cooperative lane change between the platoon columns stably and efficiently.</div></div>
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Meng, Qiang, Li Bin, Wei Pan, and Haoqi Hu. "A Novel Test Platform for Automated Vehicles Considering the Interactive Behavior of Multi-Intelligence Vehicles." In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-01-0921.

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<div class="section abstract"><div class="htmlview paragraph">With the popularity of automated vehicles, the future mixed traffic flow contains automated vehicles with different degrees of intelligence developed by other manufacturers. Therefore, simulating the interaction behavior of automated vehicles with varying levels of intelligence is crucial for testing and evaluating autonomous driving systems. Since the algorithm of traffic vehicles with various intelligence levels is difficult to obtain, it leads to hardships in quantitatively characterizing their interaction behaviors. Therefore, this paper designs a new automated vehicle test platform to solve the problem. The intelligent vehicle testbed with multiple personalized in-vehicle control units in the loop consists of three parts: 1. Multiple controllers in the loop to simulate the behavior of traffic vehicles；2. The central console applies digital twin technology to share the same traffic scenario between the tested vehicle and the traffic vehicle, creating a mixed traffic flow. 3. The tested vehicle can be either a virtual or an actual vehicle, i.e., an actual vehicle in the loop used to represent the effects of vehicle dynamics, actuators, and other characteristics.</div></div>
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Lee, T. T. "Intelligent vehicles." In 2013 IEEE International Conference on System Science and Engineering (ICSSE). IEEE, 2013. http://dx.doi.org/10.1109/icsse.2013.6614652.

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Beyerl, Thomas, Bernard Ibru, Johnnie Williams, Imani Augusma, and Valentin Soloiu. "Location Sensor Fusion and Error Correction in Intelligent Vehicles." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-67084.

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Autonomous vehicles provide an opportunity to reduce highway congestion and emissions, while increasing highway safety. Intelligently routed vehicles will also be better integrated with existing traffic patterns, minimizing travel times. By reducing the time wasted in traffic; harmful emissions will consummately be reduced. Well-designed autonomous control systems provide for increased highway safety by reducing the frequency and severity of traffic accidents caused by driver error. In order to achieve this, a robust multi-layered control system must be designed, which minimizes the likelihood of computer error, while enabling seamless transition to and from human control. Autonomous vehicle navigation systems rely on accurate and timely sensor inputs to determine a vehicle’s location, attitude, speed, and acceleration. This paper describes a telemetry sensor fusion approach, which enables an autonomous vehicle to navigate, complex intersections, based on previously planned paths and near field sensors. This reduces computational overhead on the vehicle’s computer, and provides real time redundancy for system errors or delays. In conjunction with a full complement of environmental sensors, this path planning - path following approach enhances the robustness of autonomous vehicle operating models. This research supports the rapidly expanding field of autonomous automobiles by examining novel concepts for robust telemetry sensor fusion between inertial, GPS, and wheel speed sensors, which allows for error correction and enhanced positional accuracy, when compared to conventional navigation algorithms.
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"1993 Intelligent Vehicles Symposium." In 1993 Intelligent Vehicles Symposium. IEEE, 1993. http://dx.doi.org/10.1109/ivs.1993.697289.

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Theisen, Bernard L. "The 16th annual intelligent ground vehicle competition: intelligent students creating intelligent vehicles." In IS&T/SPIE Electronic Imaging, edited by David P. Casasent, Ernest L. Hall, and Juha Röning. SPIE, 2009. http://dx.doi.org/10.1117/12.805883.

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Shen, Zhipeng, Chao Huang, Hailong Huang, Yutong Wang, Fei-Yue Wang, Abbas Jamalipour, Duc Truong Pham, Ljubo Vlacic, and Andrey V. Savkin. "The Emerging Intelligent Vehicles and Intelligent Vehicle Carriers Collaborative Systems." In 2024 IEEE Intelligent Vehicle Symposium (IV). IEEE, 2024. http://dx.doi.org/10.1109/iv55156.2024.10588476.

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Che, Yinan, Yuanbo Sun, Zhang Heng, Chenxi Wang, and Zhang Boyang. "Interactive Design for Intelligent Vehicles in the Context of Smart City." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002002.

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The progress of science and technology and the continuous development of society let us march towards an intelligent society, and the vehicle interaction mode will change accordingly. This paper aims to analyze the development of the interaction mode of the vehicle and the influencing factors of the user's travel experience, to predict the interaction mode of intelligent vehicles in the context of smart cities, and to provide some help for the relevant forward-looking design. The final conclusion is that the interaction research of smart vehicles should continue to focus on both in-vehicle interaction and out-of-vehicle interaction. The positioning of vehicles has changed: from travel tools to intelligent space development, interaction mode from two-dimensional plane to three-dimensional space development, there is a traditional single task of a single interaction mode to multi-scene co-existence, multi-modal interaction development. Users' social, entertainment and personalization needs in the vehicle will influence the overall travel experience of the user.
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Theisen, Bernard L. "The 13thAnnual Intelligent Ground Vehicle Competition: intelligent ground vehicles created by intelligent teams." In Optics East 2005, edited by David P. Casasent, Ernest L. Hall, and Juha Röning. SPIE, 2005. http://dx.doi.org/10.1117/12.630181.

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Agnew, William G., Gerald R. Lane, and Ka C. Cheok. "Intelligent Vehicles Designed by Intelligent Students." In SAE 2002 World Congress & Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2002. http://dx.doi.org/10.4271/2002-01-0404.

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Reports on the topic "Intelligent vehicles"

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Innocenti, Mario, Lorenzo Pollini, and Andrea Bracci. Intelligent Control Management of Autonomous Air Vehicles. Fort Belvoir, VA: Defense Technical Information Center, July 2006. http://dx.doi.org/10.21236/ada463037.

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Grantner, Janos, Bradley J. Bazuin, Liang Dongl, Richard Hathaway, and Claudia Fajardo. WD 08: CBM-A Intelligent Vehicle Health Management System (IVHMS) for Light Tactical Vehicles. Fort Belvoir, VA: Defense Technical Information Center, May 2010. http://dx.doi.org/10.21236/ada528800.

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Tayeb, Shahab. Protecting Our Community from the Hidden Vulnerabilities of Today’s Intelligent Transportation Systems. Mineta Transportation Institute, May 2022. http://dx.doi.org/10.31979/mti.2022.2132.

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The ever-evolving technology interwoven into the transportation industry leaves it frequently at risk for cyber-attacks. This study analyzes the security of a common in-vehicle network, the Controller Area Network (CAN), standard in most vehicles being manufactured today. Like many other networks, CAN comes with inherent vulnerabilities that leave CAN implementations at risk of being targeted by cybercriminals. Such vulnerabilities range from eavesdropping, where the attacker can read the raw data traversing the vehicle, to spoofing, where the attacker can place fabricated traffic on the network. The research team initially performed a simulation of CAN traffic generation followed by hardware implementation of the same on a test vehicle. Due to the concealed and untransparent nature of CAN, the team reverse-engineered the missing parameters through a series of passive "sniffing attacks" (attacks using data reading utilities called packet sniffers) on the network and then demonstrated the feasibility of the attack by placing fabricated frames on the CAN.
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Konaev, Margarita, Ryan Fedasiuk, Jack Corrigan, Ellen Lu, Alex Stephenson, Helen Toner, and Rebecca Gelles. U.S. and Chinese Military AI Purchases. Center for Security and Emerging Technology, August 2023. http://dx.doi.org/10.51593/20200090.

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This data brief uses procurement records published by the U.S. Department of Defense and China’s People’s Liberation Army between April and November of 2020 to assess, and, where appropriate, compare what each military is buying when it comes to artificial intelligence. We find that the two militaries are prioritizing similar application areas, especially intelligent and autonomous vehicles and AI applications for intelligence, surveillance and reconnaissance.
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Jones, Randolph M., Ron Arkin, and Nahid Sidki. Intelligent Terrain Analysis and Tactical Support System (ITATSS) for Unmanned Ground Vehicles. Fort Belvoir, VA: Defense Technical Information Center, April 2005. http://dx.doi.org/10.21236/ada434526.

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Mahlberg, Justin, Jijo Matthew, Deborah Horton, Brian McGavic, Tim Wells, and Darcy Bullock. Intelligent Sidewalk De-icing and Pre-treatment with Connected Campus Maintenance Vehicles. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317572.

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Zhang, Yangjun. Unsettled Topics Concerning Flying Cars for Urban Air Mobility. SAE International, May 2021. http://dx.doi.org/10.4271/epr2021011.

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Flying cars—as a new type of vehicle for urban air mobility (UAM)—have become an important development trend for the transborder integration of automotive and aeronautical technologies and industries. This article introduces the 100-year history of flying cars, examines the current research status for UAM air buses and air taxis, and discusses the future development trend of intelligent transportation and air-to-land amphibious vehicles. Unsettled Topics Concerning Flying Cars for Urban Air Mobility identifies the major bottlenecks and impediments confronting the development of flying cars, such as high power density electric propulsion, high lift-to-drag ratio and lightweight body structures, and low-altitude intelligent flight. Furthermore, it proposes three phased goals and visions for the development of flying cars in China, suggesting the development of a flying vehicle technology innovation system that integrates automotive and aeronautic industries.
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Kulhandjian, Hovannes. Smart Robot Design and Implementation to Assist Pedestrian Road Crossing. Mineta Transportation Institute, June 2024. http://dx.doi.org/10.31979/mti.2024.2353.

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This research focuses on designing and developing a smart robot to assist pedestrians with road crossings. Pedestrian safety is a major concern, as highlighted by the high annual rates of fatalities and injuries. In 2020, the United States recorded 6,516 pedestrian fatalities and approximately 55,000 injuries, with children under 16 being especially vulnerable. This project aims to address this need by offering an innovative solution that prioritizes real-time detection and intelligent decision-making at intersections. Unlike existing studies that rely on traffic light infrastructure, our approach accurately identifies both vehicles and pedestrians at intersections, creating a comprehensive safety system. Our strategy involves implementing advanced Machine Learning (ML) algorithms for real-time detection of vehicles, pedestrians, and cyclists. These algorithms, executed in Python, leverage data from LiDAR and video cameras to assess road conditions and guide pedestrians and cyclists safely through intersections. The smart robot, powered by ML insights, will make intelligent decisions to ensure a safer and more secure road crossing experience for pedestrians and cyclists. This project is a pioneering effort in holistic pedestrian safety, ensuring robust detection capabilities and intelligent decision-making.
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Fowler, Camilla. Automation in transport - Leading the UK to a driverless future. TRL, July 2021. http://dx.doi.org/10.58446/tawj9464.

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The gap between technology development and automated vehicle deployment has been underestimated and the challenges involved with delivering autonomy have been far greater and more complex than first envisaged. TRL believe that in order for the UK to achieve its potential for automation in transport, the following activities are key in overcoming these challenges: Develop a UK regulatory approval system that enables the safe and secure deployment of automated vehicles in the future. A flexible and responsive regulatory system is needed that can enable innovation by streamlining entry into emerging markets and lessen the initial regulatory burden on developers and manufacturers. Provide a simple, consistent but robust approach to assuring safety during trials and testing to enable and facilitate trials across all UK locations and environments. The approach to safety assurance varies between stakeholders and this inconsistency can provide a barrier to testing in multiple locations or avoiding areas with more stringent requirements. TRL is developing a software tool that could be used to guide and support stakeholders when engaging with trialling organisations. Develop and implement a UK safety monitoring and investigation unit to monitor safety, analyse data, investigate incidents and provide timely feedback and recommended actions. TRL can identify road user behaviours that are likely to lead to a collision. These behaviours could be monitored using in-vehicle data and supplemented with environmental and location data from intelligent infrastructure. This proactive approach would drive safety improvements, promote continuous improvement, accelerate innovation and development and make Vision Zero a more realistic and achievable target. Enable more advanced trials to be undertaken in the UK where the boundaries of the technology are extended and solutions to the identified challenges are explored without compromising safety. London’s Smart Mobility Living Lab (SMLL) provides a unique real-world test facility to conduct advanced tests and validate vehicle behaviour performance. Through testing in a real-world environment and monitoring performance using cooperative infrastructure, we can accelerate learning and technology progression. Accelerate the adoption and safe implementation of automated vehicles for off- highway activities and minimise worker exposure to high risk environments and working practices within the UK and globally. As part of an Innovate funded project on Automated Off-highway Vehicles, TRL has developed and published a draft Code of Practice providing guidance to operators of automated vehicles in all sectors of the off-highway industry.
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Kumar, Ratnesh, and Lawrence E. Holloway. DEPSCOR: Research on ARL's Intelligent Control Architecture: Hierarchical Hybrid-Model Based Design, Verification, Simulation, and Synthesis of Mission Control for Autonomous Underwater Vehicles. Fort Belvoir, VA: Defense Technical Information Center, February 2007. http://dx.doi.org/10.21236/ada464977.

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