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Journal articles on the topic 'Intelligent vehicles'
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1
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.
2
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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Özkul, Mükremin, Ilir Capuni, and Elton Domnori. "Context-Aware Intelligent Traffic Light Control through Secure Messaging." Journal of Advanced Transportation 2018 (November 5, 2018): 1–10. http://dx.doi.org/10.1155/2018/4251701.
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In this paper, we propose STCM, a context-aware secure traffic control model to manage competing traffic flows at a given intersection by using secure messages with real-time traffic information. The vehicle is modeled as a virtual sensor which reports the traffic state, such as its speed and location, to a traffic light controller through a secure and computationally lightweight protocol. During the reporting process, a vehicle’s identity and location are kept anonymous to any other vehicle in the system. At an intersection, the traffic light controller receives the messages with traffic information, verifies the identities of the vehicles, and dynamically implements and optimizes the traffic light phases in real-time. Moreover, the system is able to detect the presence of emergency vehicles (such as ambulances and fire fighting trucks) in the communication range and prioritize the intersection crossing of such vehicles to in order to minimize their waiting times. The simulation results demonstrate that the system significantly reduces the waiting time of the vehicles in both light and heavy traffic flows compared to the pretimed signal control and the adaptive Webster’s method. Simulation results also yield effective robustness against impersonating attacks from malicious vehicles.
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Bathla, Gourav, Kishor Bhadane, Rahul Kumar Singh, Rajneesh Kumar, Rajanikanth Aluvalu, Rajalakshmi Krishnamurthi, Adarsh Kumar, R. N. Thakur, and Shakila Basheer. "Autonomous Vehicles and Intelligent Automation: Applications, Challenges, and Opportunities." Mobile Information Systems 2022 (June 6, 2022): 1–36. http://dx.doi.org/10.1155/2022/7632892.
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Intelligent Automation (IA) in automobiles combines robotic process automation and artificial intelligence, allowing digital transformation in autonomous vehicles. IA can completely replace humans with automation with better safety and intelligent movement of vehicles. This work surveys those recent methodologies and their comparative analysis, which use artificial intelligence, machine learning, and IoT in autonomous vehicles. With the shift from manual to automation, there is a need to understand risk mitigation technologies. Thus, this work surveys the safety standards and challenges associated with autonomous vehicles in context of object detection, cybersecurity, and V2X privacy. Additionally, the conceptual autonomous technology risks and benefits are listed to study the consideration of artificial intelligence as an essential factor in handling futuristic vehicles. Researchers and organizations are innovating efficient tools and frameworks for autonomous vehicles. In this survey, in-depth analysis of design techniques of intelligent tools and frameworks for AI and IoT-based autonomous vehicles was conducted. Furthermore, autonomous electric vehicle functionality is also covered with its applications. The real-life applications of autonomous truck, bus, car, shuttle, helicopter, rover, and underground vehicles in various countries and organizations are elaborated. Furthermore, the applications of autonomous vehicles in the supply chain management and manufacturing industry are included in this survey. The advancements in autonomous vehicles technology using machine learning, deep learning, reinforcement learning, statistical techniques, and IoT are presented with comparative analysis. The important future directions are offered in order to indicate areas of potential study that may be carried out in order to enhance autonomous cars in the future.
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Fernández Llorca, David, Iván García Daza, Noelia Hernández Parra, and Ignacio Parra Alonso. "Sensors and Sensing for Intelligent Vehicles." Sensors 20, no. 18 (September 8, 2020): 5115. http://dx.doi.org/10.3390/s20185115.
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Over the past decades, both industry and academy have made enormous advancements in the field of intelligent vehicles, and a considerable number of prototypes are now driving our roads, railways, air and sea autonomously. However, there is still a long way to go before a widespread adoption. Among all the scientific and technical problems to be solved by intelligent vehicles, the ability to perceive, interpret, and fully understand the operational environment, as well as to infer future states and potential hazards, represent the most difficult and complex tasks, being probably the main bottlenecks that the scientific community and industry must solve in the coming years to ensure the safe and efficient operation of the vehicles (and, therefore, their future adoption). The great complexity and the almost infinite variety of possible scenarios in which an intelligent vehicle must operate, raise the problem of perception as an "endless" issue that will always be ongoing. As a humble contribution to the advancement of vehicles endowed with intelligence, we organized the Special Issue on Intelligent Vehicles. This work offers a complete analysis of all the mansucripts published, and presents the main conclusions drawn.
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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.
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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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Wang, Biyao, Yi Han, Siyu Wang, Di Tian, Mengjiao Cai, Ming Liu, and Lujia Wang. "A Review of Intelligent Connected Vehicle Cooperative Driving Development." Mathematics 10, no. 19 (October 4, 2022): 3635. http://dx.doi.org/10.3390/math10193635.
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With the development and progress of information technology, especially V2X technology, the research focus of intelligent vehicles gradually shifted from single-vehicle control to multi-vehicle control, and the cooperative control system of intelligent connected vehicles became an important topic of development. In order to track the research progress of intelligent connected vehicle cooperative driving systems in recent years, this paper discusses the current research of intelligent connected vehicle cooperative driving systems with vehicles, infrastructure, and test sites, and analyzes the current development status, development trend, and development limitations of each object. Based on the analysis results of relevant references of the cooperative control algorithm, this paper expounds on vehicle collaborative queue control, vehicle collaborative decision making, and vehicle collaborative positioning. In the case of taking the infrastructure as the object, this paper expounds the communication security, communication delay, and communication optimization algorithm of the vehicle terminal and the road terminal of intelligent connected vehicles. In the case of taking the test site as the object, this paper expounds the development process and research status of the real vehicle road test platform, virtual test platform, test method, and evaluation mechanism, and analyzes the problems existing in the intelligent connected vehicle test environment. Finally, the future development trend and limitations of intelligent networked vehicle collaborative control system are discussed. This paper summarizes the intelligent connected car collaborative control system, and puts forward the next problems to be solved and the direction of further exploration. The research results can provide a reference for the cooperative driving of intelligent vehicles.
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Malerczyk, Jessica, Sabine Lerch, Bernd Tibken, and Anton Kummert. "Impact of intelligent agents on the avoidance of spontaneous traffic jams on two-lane motorways." MATEC Web of Conferences 308 (2020): 05003. http://dx.doi.org/10.1051/matecconf/202030805003.
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This paper approaches the evaluation of intelligent agents for the reduction and avoidance of spontaneous traffic jams, which arise without evident reason. Individual vehicles are regarded as intelligent agents that act autonomously. The basis of this work is the Nagel-Schreckenberg (NaSch) model. Its extensions by the velocity-dependent randomization (VDR) model and multiple lanes allow us to simulate realistic traffic and congestion situations on two-lane motorways. Our concept is applied to the model and analyzed by fundamental diagrams and the average velocity, for example. The results of this paper reveal that traffic congestions are avoided when using swarm intelligence in all vehicles since human behavior, especially misbehavior, is eliminated and the velocities determined by the intelligent vehicle are directly realized. Moreover, an amount of 30% of intelligent vehicles has a significantly positive impact on traffic flow.
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Deng, Zhaoxue, Yangrui Zhang, and Shuen Zhao. "Distributed Intelligent Vehicle Path Tracking and Stability Cooperative Control." World Electric Vehicle Journal 15, no. 3 (February 28, 2024): 89. http://dx.doi.org/10.3390/wevj15030089.
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To enhance the path tracking capability and driving stability of intelligent vehicles, a controller is designed that synergizes active front wheel steering (AFS) and direct yaw moment (DYC), specifically tailored for distributed-drive electric vehicles. To address the challenge of determining the weight matrix in the linear quadratic regulator (LQR) algorithm during the path tracking design for intelligent vehicles on conventional roads, a genetic algorithm (GA)-optimized LQR path tracking controller is introduced. The 2-degree-of-freedom vehicle dynamics error model and the desired path information are established. The genetic algorithm optimization strategy, utilizing the vehicle’s lateral error, heading error, and output front wheel steering angle as the objective functions, is employed to optimally determine the weight matrices Q and R. Subsequently, the optimal front wheel steering angle control (AFS) output of the vehicle is calculated. Under extreme operating conditions, to enhance vehicle dynamics stability, while ensuring effective path tracking, the active yaw moment is crafted using the sliding mode control with a hyperbolic tangent convergence law function. The control weights of the sliding mode surface related to the center-of-mass lateral declination are adjusted based on the theory of the center-of-mass lateral declination phase diagram, and the vehicle’s target yaw moment is calculated. Validation is conducted through Matlab/Simulink and Carsim co-simulation. The results demonstrate that the genetic algorithm-optimized LQR path tracking controller enhances vehicle tracking accuracy and exhibits improved robustness under conventional road conditions. In extreme working conditions, the designed path tracking and stability cooperative controller (AFS+DYC) is implemented to enhance the vehicle’s path tracking effect, while ensuring its driving stability.
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Jin, Li Qiang, Chuan Xue Song, and Jian Hua Li. "Intelligent Velocity Control Strategy for Electric Vehicles." Applied Mechanics and Materials 80-81 (July 2011): 1180–84. http://dx.doi.org/10.4028/www.scientific.net/amm.80-81.1180.
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In conventional vehicles, the control of vehicle speed is achieved by changing the engine load through adjusting the acceleration pedal. However, in electric vehicles, this is achieved by controlling the target motor torque obtained from the look-up table in accordance with the position of acceleration pedal. This method is an open-loop control, with which the engine brake cannot be implemented during downhill trips. In this paper, a closed-loop control of vehicle speed for electric vehicles is proposed. The target vehicle speed is set by the acceleration pedal. The controller collects the real vehicle speed, whereas the PID controller, according to the error of the real and target vehicle speed, adjusts the motor torque in real time to realize the closed-loop speed control. Under such controlling, the motor torque can be changed correspondingly with the resistance, thus makes the driving performance of electric vehicles more identical to that of conventional vehicles.
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Bian, Chentong, Guodong Yin, Liwei Xu, and Ning Zhang. "REAR-END COLLISION ESCAPE ALGORITHM FOR INTELLIGENT VEHICLES SUPPORTED BY VEHICULAR COMMUNICATION." Transport 37, no. 6 (December 31, 2022): 398–410. http://dx.doi.org/10.3846/transport.2022.18172.
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To reduce rear-end collision risks and improve traffic safety, a novel rear-end collision escape algorithm is proposed for intelligent vehicles supported by vehicular communication. Numerous research has been carried out on rear-end collision avoidance. Most of these studies focused on maintaining a safe front clearance of a vehicle while only few considered the vehicle’s rear clearance. However, an intelligent vehicle may be collided by a following vehicle due to wrong manoeuvres of an unskilled driver of the following vehicle. Hence, it is essential for an intelligent vehicle to maintain a safe rear clearance when there is potential for a rear-end collision caused by a following vehicle. In this study, a rear-end collision escape algorithm is proposed to prevent rear-end collisions by a following vehicle considering both straight and curved roads. A trajectory planning method is designed according to the motions of the considered intelligent vehicle and the corresponding adjacent vehicles. The successive linearization and the Model Predictive Control (MPC) algorithms are used to design a motion controller in the proposed algorithm. Simulations were performed to demonstrate the effectiveness of the proposed algorithm. The results show that the proposed algorithm is effective in preventing rear-end collisions caused by a following vehicle.
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Mai, Yi Ting, Jeng Yueng Chen, Yi Kuan Liu, Wen Yi Lee, Guan Ting Wu, and Ming Yuan Li. "Intelligent Vehicular Warning System for VANET." Applied Mechanics and Materials 145 (December 2011): 164–68. http://dx.doi.org/10.4028/www.scientific.net/amm.145.164.
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The vehicular ad hoc network (VANET) has made significant progress in recent years, attracting a lot of interest from academia and the industry. VANET involves vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications based on a wireless network. V2I refers to the communication between vehicles and infrastructure of roadside unit (RSU), e.g., a base station and access point (AP) connected to the Internet. V2V refers to the direct or multi-hop communications among vehicles in VANET. V2V is efficient and cost effective owing to its short range bandwidth advantage and its ad hoc nature. V2V communications are enabling technologies that enhance the driver’s awareness of nearby vehicular traffic, leading to improved traffic safety and efficiency. The V2V mode provides a communications platform between road vehicles (cars, bikes, scooters, motorcycles, trucks, etc.) without requiring a central control unit. Safety-related V2V applications are enabled via an integrated early warning mechanism. To facilitate safe driving, we propose an Intelligent Vehicular Warning System (IVWS) that sends an immediate warning message in the event of an accident. According to V2V communications, the other cars or vehicles could have enough time to avoid the accident and make an appropriate decision such as slow down, stop, and detour after receiving the urgent warning messages. Furthermore, the local CMS (Changeable Message Sign) can show the accident information for neighbor vehicles when receiving the warning message. To achieve experimental architecture with our proposed IVWS, the robot vehicles have been designed to simulate vehicles on the road. Besides, vehicles also apply ZigBee wireless interface to communicate with each other. The experiment has shown that our proposed intelligent system can initially provide message display and safety driving for vehicles when traffic accident occurred.
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Liu, Zongwei, Hong Tan, Xu Kuang, Han Hao, and Fuquan Zhao. "The Negative Impact of Vehicular Intelligence on Energy Consumption." Journal of Advanced Transportation 2019 (July 24, 2019): 1–11. http://dx.doi.org/10.1155/2019/1521928.
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The development of intelligent vehicle will provide the Chinese automotive industry with a strategic opportunity for transformation and upgrading. Vehicular intelligence provides new solutions for energy conservation and emissions mitigation. However, the process of vehicular intelligence is progressive. The saving of energy consumption depends on the high smart car market penetration rate. But one thing that can be confirmed is that intelligent vehicles are equipped with advanced sensors, controllers, and actuators, in combination with connecting communication technologies compared with conventional vehicles, for which the energy consumption of the vehicle will definitely increase. In this study, vehicle fuel consumption cost at different levels of intelligence is calculated, considering the energy consumption of hardware used for automation and connecting functions, the energy consumption cost generated by the quality of the hardware, and the wind resistance. The results reveal that the energy consumption per 100 kilometers of an intelligent vehicle ranges from 0.78L to 1.86L, more than traditional vehicle. The energy consumption cost of automation functions is much higher than that of the connecting functions. Computing platform performance, connection strength, and radar performance are the three main factors that affect energy consumption cost. Based on the analysis, the high energy consumption cost of vehicular intelligence has a profound impact on choosing power platform.
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Saputro, Joko Slamet, Miftahul Anwar, Feri Adriyanto, Agus Ramelan, Putra Maulana Yusuf, Fakih Irsyadi, Rendra Dwi Firmansyah, and Tri Wahyu Oktaviana Putri. "Design of intelligent cruise control system using fuzzy-PID control on autonomous electric vehicles prototypes." Journal of Mechatronics, Electrical Power, and Vehicular Technology 15, no. 1 (July 31, 2024): 105–16. http://dx.doi.org/10.55981/j.mev.2024.877.
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Electric vehicles provide a solution for using alternative fuels, namely, electricity. Electric vehicles are used for short distances and intercity travel over long distances, increasing the risk of accidents. Cruise Control is a technology embedded in vehicles to maintain stable speeds; this system will automatically adjust the vehicle's speed when motion changes cause changes in vehicle speed. This study aims to apply lidar sensors to detect distance in the Intelligent Cruise Control (ICC) system using the Fuzzy-PID control method. Testing results were obtained at safe distance inputs of 5, 6, and 7 meters with various object distances. All the tests were carried out; the response systems were obtained with an average settling time of 5 seconds and an average overshoot of 1.53%. Therefore, the proposed Fuzzy-PID method works well for controlling Intelligent Cruise Control systems in autonomous electric vehicle prototypes.
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Migal, Vasiliy, Shchasiana Arhun, Andrii Hnatov, Hanna Hnatova, and Pavlo Sokhin. "Intelligent diagnosics of vehicles." Vehicle and electronics. Innovative technologies, no. 22 (December 27, 2022): 72–80. http://dx.doi.org/10.30977/veit.2022.22.0.5.
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Problem. Diagnostics or troubleshooting is an integral part of the operation of automotive technology, and as automotive systems become more complex, the need for diagnostic skills increases, so diagnostic methods by the human senses should be considered an integral part of technical diagnostics at all stages of a vehicle life cycle. Methodology. Analytical methods are used to study the methods of diagnosing vehicles with the help of the intellectual abilities of the operator-diagnostician. Results. The paper shows that the intellectual abilities of the operator-diagnostician play an important role in diagnosing vehicles, the advantages and disadvantages of such diagnostics are presented. The list of basic knowledge necessary for the operator-diagnostician is described as well as the type of operational documentation which is necessary to improve the efficiency of intelligent diagnostics. Intelligent diagnostics of vehicles is divided into stages and shows the wide possibilities of diagnosing by the senses and knowledge of the diagnostician. It is shown that a highly qualified diagnostician can significantly reduce the complexity of diagnosis. With qualified training, experienced mechanics determine up to 70-90% of malfunctions and failures of vehicles and units using organoleptic methods and simple tests. Originality. The stages of intelligent diagnostics of vehicles are singled out and the wide possibilities of diagnosing by the human senses and knowledge of diagnostics at these stages are shown. Practical value. The results of this work are intended for wide use, for example, for drivers, maintenance services, developers of operational and technical documentation, developers involved in the improvement of technical diagnostic tools, machine learning, etc.
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Ruan, Xian Jing. "Design of New Energy Vehicle Intelligent Anti-Collision System." Advanced Materials Research 1028 (September 2014): 234–38. http://dx.doi.org/10.4028/www.scientific.net/amr.1028.234.
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The new energy vehicle intelligence anti-collision system, is a new energy vehicle safety assist device , can timely and intuitive display the obstacles of new energy vehicles’ around, help the driver to remove the dead angle of vision and the flaw of sight, in case of emergency, intelligent control of brake. The research of this paper is that new energy vehicle intelligent anti-collision system of a kind of low cost, high precision, miniaturization and with a liquid crystal display and sound , light alarm function center on the single chip AT89S52 , the product is developed according to the principle of ultrasonic ranging, using the temperature compensation technology, power on self-checking technology and optimized hardware and software technology, sent the measured results to the liquid crystal display, in case of emergency, intelligent control of brake, improves the safety and efficiency of new energy vehicles.
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Gao, Hongbo, Xinyu Zhang, Yuchao Liu, and Deyi Li. "Cloud Model Approach for Lateral Control of Intelligent Vehicle Systems." Scientific Programming 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/6842891.
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Studies on intelligent vehicles, among which the controlling method of intelligent vehicles is a key technique, have drawn the attention of industry and the academe. This study focuses on designing an intelligent lateral control algorithm for vehicles at various speeds, formulating a strategy, introducing the Gauss cloud model and the cloud reasoning algorithm, and proposing a cloud control algorithm for calculating intelligent vehicle lateral offsets. A real vehicle test is applied to explain the implementation of the algorithm. Empirical results show that if the Gauss cloud model and the cloud reasoning algorithm are applied to calculate the lateral control offset and the vehicles drive at different speeds within a direction control area of ±7°, a stable control effect is achieved.
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Wang, Qiong, Yan Yu, and Zhen Min Tang. "Architecture Design for Intelligent Vehicle Computing Platform Based on Internet of Vehicles." Applied Mechanics and Materials 253-255 (December 2012): 1423–26. http://dx.doi.org/10.4028/www.scientific.net/amm.253-255.1423.
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Traffic congestion, accidents and caused environment pollution have become the common problems faced by the world. So how to improve traffic of city has become the focus of the global concern. The development of intelligent computing, Internet of Vehicles, wireless networks and so forth have provided a new idea for solving the increasing transportation issue in cities. Intelligent vehicle computing platform is key component of Internet of Vehicles. In this paper, we discussed the intelligent vehicle computing technology and proposed the architecture of intelligent vehicle computing platform.
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Wang, Ji Zhe, and Zhan Jie Wang. "Architecture Design of Urban Intelligent Transportation Using Cloud Computing." Advanced Materials Research 605-607 (December 2012): 2549–52. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.2549.
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This article does a further research on the technologies of cloud computing and intelligent public transportation, designs a human-centered intelligent public transportation system, improves the connection between passengers and vehicles, vehicles and vehicles. Technologies of BeiDou messaging, capacity sensor, RFID and Web are used to improve the intelligence of public transportation information platform. This article makes an architecture design in intelligent transportation based on cloud computing and uses the embedded system as the core. The architecture allows passengers to obtain needed vehicles’ information through various services whenever and wherever, and strongly supports intelligent control and schedule of multi-vehicles and multi-lines. The system lays a good foundation for the intelligent city.
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Mansour, Ayman M. "Cooperative Multi-Agent Vehicle-to-Vehicle Wireless Network in a Noisy Environment." International Journal of Circuits, Systems and Signal Processing 15 (February 22, 2021): 135–48. http://dx.doi.org/10.46300/9106.2021.15.15.
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With the rapid development of vehicle communication and the goal of self-driving vehicle, research in this area is still ongoing, as car companies aspire for more studies and effective communication methods between vehicles. In this research, we have developed an intelligent, innovative and fully integrated multi agent model, which is used for vehicle-to-vehicle communications. The developed model is supported by an intelligent system based on a Nonlinear External Neural Network (NARX) and signal estimation theory. The system is built using real vehicles sensors, Arduino, GSM and RF technologies. The system is tested by applying different scenarios and observing vehicle behaviors. The results show that the smart system is able to make the appropriate decision based on both the vehicle's current condition and sensor readings. The developed system is able to operate effectively in a noisy environment in an excellent manner.
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Tian, M. Z., L. Liu, J. Y. Lu, and Y. Cheng. "Vehicle recognition based on Haar features and Adaboost cascade classifier." Journal of Physics: Conference Series 2303, no. 1 (July 1, 2022): 012052. http://dx.doi.org/10.1088/1742-6596/2303/1/012052.
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Abstract With the progress of world science and economic development, more and more industries are developing towards intelligence and automation. In the field of intelligent driving, the intelligent vehicle environment perception method based on machine vision has become a hot research topic. Based on monocular vision system, aiming at the requirements of different target features and detection accuracy and efficiency, this paper improves the Haar feature and Adaboost cascade classifier recognition algorithm combined with gray symmetry method to adapt to the recognition environment required by vehicles. The measured results show that the improved vehicle identification method combined with the tracking method based on Kalman filter can reduce the misjudgment rate of vehicles and has good real-time performance.
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Chohra, Amine, and Ouahiba Azouaoui. "Navigation Behaviors Based on Fuzzy ArtMap Neural Networks for Intelligent Autonomous Vehicles." Advances in Artificial Neural Systems 2011 (December 8, 2011): 1–11. http://dx.doi.org/10.1155/2011/523094.
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The use of hybrid intelligent systems (HISs) is necessary to bring the behavior of intelligent autonomous vehicles (IAVs) near the human one in recognition, learning, adaptation, generalization, decision making, and action. First, the necessity of HIS and some navigation approaches based on fuzzy ArtMap neural networks (FAMNNs) are discussed. Indeed, such approaches can provide IAV with more autonomy, intelligence, and real-time processing capabilities. Second, an FAMNN-based navigation approach is suggested. Indeed, this approach must provide vehicles with capability, after supervised fast stable learning: simplified fuzzy ArtMap (SFAM), to recognize both target-location and obstacle-avoidance situations using FAMNN1 and FAMNN2, respectively. Afterwards, the decision making and action consist of two association stages, carried out by reinforcement trial and error learning, and their coordination using NN3. Then, NN3 allows to decide among the five (05) actions to move towards 30∘, 60∘, 90∘, 120∘, and 150∘. Third, simulation results display the ability of the FAMNN-based approach to provide IAV with intelligent behaviors allowing to intelligently navigate in partially structured environments. Finally, a discussion, dealing with the suggested approach and how its robustness would be if implemented on real vehicle, is given.
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Cao, Yaoguang, Yuyi Chen, and Lu Liu. "Research prospect of autonomous driving decision technology under complex traffic scenarios." MATEC Web of Conferences 355 (2022): 03031. http://dx.doi.org/10.1051/matecconf/202235503031.
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Decision-making system is the essential part of the autonomous vehicle “brain”, which determines the safety and stability of vehicles, and is also the key to reflect the intelligent level of autonomous vehicles. Compared with simple scenarios such as expressway, urban traffic scenarios have the characteristics of complex and frequent interaction between traffic participants. Carrying out in-depth research on complex traffic scenarios and optimizing autonomous decision-making algorithms are the key methods for the purpose of promoting the application of autonomous driving technologies. In the future, we can further combine the artificial intelligence methods such as cognitive or knowledge map, behaviour prediction of traffic participants, and humanoid intelligence, so as to enhance the intelligent level of autonomous driving.
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Wang, Feng, and Zhaofeng Zhang. "Route Control and Behavior Decision of Intelligent Driverless Truck Based on Artificial Intelligence Technology." Wireless Communications and Mobile Computing 2022 (September 7, 2022): 1–10. http://dx.doi.org/10.1155/2022/7025081.
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With the increase in global car ownership, the demand for traffic safety is very strong. Research shows that drivers account for more than 90% of global traffic accidents. Driverless cars can reduce traffic accidents caused for these reasons and greatly improve traffic safety. At the same time, driverless real-time path planning can select the best driving route for vehicles, reduce traffic congestion, and improve the efficiency of transportation. To sum up, driverless vehicles are considered an important solution to ensure traffic safety, improve traffic efficiency, reduce energy consumption and pollution, and change travel mode. An intelligent driverless vehicle is a key component of the intelligent transportation system, which organically combines various functions such as. Among them, path tracking and motion control play a very important role in intelligent driverless technology. At the same time, accurately tracking the desired feasible path and stable motion control are the basis of intelligent unmanned driving. Based on this, this paper uses artificial intelligence technology to study the path control and behavior decision-making of intelligent driverless trucks, and an improved tracking control method is proposed. Through this improved method, the intelligent unmanned vehicle can track the desired feasible path under different curvatures more accurately and stably. Finally, through the road test experiment of the intelligent unmanned vehicle experimental platform in the actual environment, the effectiveness of the scheme design and related algorithms of intelligent unmanned vehicle motion control in this paper is verified.
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Li, Lin, Zeyu Hu, and Xubo Yang. "Intelligent Analysis of Abnormal Vehicle Behavior Based on a Digital Twin." Journal of Shanghai Jiaotong University (Science) 26, no. 5 (October 2021): 587–97. http://dx.doi.org/10.1007/s12204-021-2348-7.
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AbstractAnalyzing a vehicle’s abnormal behavior in surveillance videos is a challenging field, mainly due to the wide variety of anomaly cases and the complexity of surveillance videos. In this study, a novel intelligent vehicle behavior analysis framework based on a digital twin is proposed. First, detecting vehicles based on deep learning is implemented, and Kalman filtering and feature matching are used to track vehicles. Subsequently, the tracked vehicle is mapped to a digital-twin virtual scene developed in the Unity game engine, and each vehicle’s behavior is tested according to the customized detection conditions set up in the scene. The stored behavior data can be used to reconstruct the scene again in Unity for a secondary analysis. The experimental results using real videos from traffic cameras illustrate that the detection rate of the proposed framework is close to that of the state-of-the-art abnormal event detection systems. In addition, the implementation and analysis process show the usability, generalization, and effectiveness of the proposed framework.
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Zhou, Yunya, Yang He, Yu Yan, Fang Li, Neng Li, Chaofeng Zhang, Zijian Lu, and Zhiyong Yang. "Autonomous charging docking control method for unmanned vehicles based on vision and infrared." Journal of Physics: Conference Series 2584, no. 1 (September 1, 2023): 012065. http://dx.doi.org/10.1088/1742-6596/2584/1/012065.
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Abstract Unmanned vehicle charging is part of the autonomous workflow of unmanned vehicles. Most existing unmanned vehicles mostly rely on manual battery change or manual charging, which cannot realize autonomous charging. In order to achieve simpler and safer autonomous charging for unmanned vehicles, this paper proposes a new intelligent unmanned vehicle autonomous charging docking method based on infrared guidance and vision assistance. Firstly, the autonomous charging device and intelligent charging stand for unmanned vehicles are designed, and for the unmanned vehicle, charging is not easy to align and easy to detach when charging. The camber-type electric core and charging adsorption device are designed, respectively, and the autonomous charging docking device is designed. Secondly, in order to ensure the accuracy of the docking between the unmanned vehicle and the intelligent charging stand, the unmanned vehicle autonomous charging method is proposed. The combination method of infrared and vision adjusts the posture of the unmanned vehicle. Finally, a protection method of autonomous charging docking based on ultrasonic ranging of unmanned vehicles is proposed. The communication and ranging modules on the unmanned vehicle and intelligent charging stand are designed to prevent mistouching and obstacle avoidance to ensure the safety of the system. The experiment results show that the docking method of this unmanned vehicle autonomous charging system is accurate, efficient, and safe, which can satisfy the demand for unmanned vehicle autonomous charging.
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Rajashekara, Kaushik, and Sharon Koppera. "Data and Energy Impacts of Intelligent Transportation—A Review." World Electric Vehicle Journal 15, no. 6 (June 17, 2024): 262. http://dx.doi.org/10.3390/wevj15060262.
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The deployment of intelligent transportation is still in its early stages and there are many challenges that need to be addressed before it can be widely adopted. Autonomous vehicles are a class of intelligent transportation that is rapidly developing, and they are being deployed in selected cities. A combination of advanced sensors, machine learning algorithms, and artificial intelligence are being used in these vehicles to perceive their environment, navigate, and make the right decisions. These vehicles leverage extensive data sourced from various sensors and computers integrated into the vehicle. Hence, massive computational power is required to process the information from various built-in sensors in milliseconds to make the right decision. The power required by the sensors and the use of additional computational power increases the energy consumption, and, hence, could reduce the range of the autonomous electric vehicle relative to a standard electric car and lead to additional emissions. A number of review papers have highlighted the environmental benefits of autonomous vehicles, focusing on aspects like optimized driving, improved route selection, fewer stops, and platooning. However, these reviews often overlook the significant energy demands of the hardware systems—such as sensors, computers, and cameras—necessary for full autonomy, which can decrease the driving range of electric autonomous vehicles. Additionally, previous studies have not thoroughly examined the data processing requirements in these vehicles. This paper provides a more detailed review of the volume of data and energy usage by various sensors and computers integral to autonomous features in electric vehicles. It also discusses the effects of these factors on vehicle range and emissions. Furthermore, the paper explores advanced technologies currently being developed by various industries to enhance processing speeds and reduce energy consumption in autonomous vehicles.
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Huang, Hsu-Chih, Chin-Wang Tao, Chen-Chia Chuang, and Jing-Jun Xu. "FPGA-Based Mechatronic Design and Real-Time Fuzzy Control with Computational Intelligence Optimization for Omni-Mecanum-Wheeled Autonomous Vehicles." Electronics 8, no. 11 (November 11, 2019): 1328. http://dx.doi.org/10.3390/electronics8111328.
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This study presents a field-programmable gate array (FPGA)-based mechatronic design and real-time fuzzy control method with computational intelligence optimization for omni-Mecanum-wheeled autonomous vehicles. With the advantages of cuckoo search (CS), an evolutionary CS-based fuzzy system is proposed, called CS-fuzzy. The CS’s computational intelligence was employed to optimize the structure of fuzzy systems. The proposed CS-fuzzy computing scheme was then applied to design an optimal real-time control method for omni-Mecanum-wheeled autonomous vehicles with four wheels. Both vehicle model and CS-fuzzy optimization are considered to achieve intelligent tracking control of Mecanum mobile vehicles. The control parameters of the Mecanum fuzzy controller are online-adjusted to provide real-time capability. This methodology outperforms the traditional offline-tuned controllers without computational intelligences in terms of real-time control, performance, intelligent control and evolutionary optimization. The mechatronic design of the experimental CS-fuzzy based autonomous mobile vehicle was developed using FPGA realization. Some experimental results and comparative analysis are discussed to examine the effectiveness, performance, and merit of the proposed methods against other existing approaches.
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Sun, Yang, Guang Ming Xiong, Hui Yan Chen, Shao Bin Wu, Jian Wei Gong, and Yan Jiang. "A Cost Function-Oriented Quantitative Evaluation Method for Unmanned Ground Vehicles." Advanced Materials Research 301-303 (July 2011): 701–6. http://dx.doi.org/10.4028/www.scientific.net/amr.301-303.701.
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Task driven approach is widely used as an evaluation method for intelligent vehicle, however this approach may lead to many teams using the conservative approach to complete the task. Although the competition task can be completed, it has deviated from the goal of technological development actually. A cost function-oriented quantitative evaluation method is proposed in this study. The time to complete each task and the quality of each indicator are considered in the evaluation method. The cost function-oriented quantitative evaluation method guides the intelligent vehicle’s development in the "low-cost index" (i.e. high technology) direction. The evaluation results in the 2010 Future Challenge: Intelligent Vehicles and Beyond (FC’ 2010) competition showed that the proposed method can quantitatively evaluate the overall technical performance and individual technical performance of unmanned vehicles.
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Ma, Jiandong. "Design of Intelligent Vehicle Monitoring System Based on ZigBee." MATEC Web of Conferences 173 (2018): 02026. http://dx.doi.org/10.1051/matecconf/201817302026.
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In order to meet the needs of real-time positioning and remote dispatching of vehicle, this paper designs a ZigBee-based embedded vehicle terminal and the corresponding ZigBee-GPRS information communication network in hardware and software. With LPC2366 processor and CC2430 RF chip as core, the vehicle terminal acquires the vehicle‘s status in cycle, and completes the vehicle monitoring and scheduling by transmitting data to communication nodes by ZigBee and passing the data on to the monitoring center by GPRS. This vehicle terminal is characterized by small size, low energy consumption and ideal communication distance, which makes the monitoring center effectively monitor and schedule the vehicles.
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Fayyazi, Mojgan, Paramjotsingh Sardar, Sumit Infent Thomas, Roonak Daghigh, Ali Jamali, Thomas Esch, Hans Kemper, Reza Langari, and Hamid Khayyam. "Artificial Intelligence/Machine Learning in Energy Management Systems, Control, and Optimization of Hydrogen Fuel Cell Vehicles." Sustainability 15, no. 6 (March 15, 2023): 5249. http://dx.doi.org/10.3390/su15065249.
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Environmental emissions, global warming, and energy-related concerns have accelerated the advancements in conventional vehicles that primarily use internal combustion engines. Among the existing technologies, hydrogen fuel cell electric vehicles and fuel cell hybrid electric vehicles may have minimal contributions to greenhouse gas emissions and thus are the prime choices for environmental concerns. However, energy management in fuel cell electric vehicles and fuel cell hybrid electric vehicles is a major challenge. Appropriate control strategies should be used for effective energy management in these vehicles. On the other hand, there has been significant progress in artificial intelligence, machine learning, and designing data-driven intelligent controllers. These techniques have found much attention within the community, and state-of-the-art energy management technologies have been developed based on them. This manuscript reviews the application of machine learning and intelligent controllers for prediction, control, energy management, and vehicle to everything (V2X) in hydrogen fuel cell vehicles. The effectiveness of data-driven control and optimization systems are investigated to evolve, classify, and compare, and future trends and directions for sustainability are discussed.
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ENDACHEV, Denis V., Sergey V. BAKHMUTOV, Vladimir V. EVGRAFOV, and Nikolay P. MEZENTCEV. "ELECTRONIC SYSTEMS OF INTELLIGENT VEHICLES." Mechanics of Machines, Mechanisms and Materials 4, no. 53 (December 2020): 5–10. http://dx.doi.org/10.46864/1995-0470-2020-4-53-5-10.
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Modern automotive engineering is closely related to the implementation of information systems. In automobile transport, the range of such developments is considerably wide: from driver assistance systems (ADAS — Advanced Driver Assistance System) to full autopilot systems. The article provides a brief overview of the state of the problem and presents the main directions of development of the State Research Center of the Russian Federation FSUE “NAMI” in the field of ADAS and highly automated (unmanned) vehicles. Descriptions of on-board vehicle systems of a high level of automation are given developed by the State Research Center of the Russian Federation FSUE “NAMI” with the participation of manufacturers. The article also describes the key technologies of machine vision systems, test sites for highly automated vehicles.
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Meng, Zhixin. "Study of Interaction Interface of Vehicles for Automatic Driving." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 451–56. http://dx.doi.org/10.62051/scqs6k83.
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With the rapid development of the vehicle and information industries, the traditional vehicle has been transformed from a simple travel tool to an intelligent vehicle with comprehensive functions, intelligence, and rich information. As a standard function of intelligent vehicles, automated driving will greatly reduce or even replace humans for vehicle control, and the human-machine interaction system pays more attention to displaying the driving status and prompting the driver in an intuitive way, to make the driver fully trust the vehicle and obtain safe driving experience. In this paper, I study the development and current application of the interaction interface of vehicles. The main study objective is HUD (Head-Up Display). By introducing different technologies of HUDs, the study describes the examples of interaction interface of HUDs, analyzes the pros and cons of the different applications, and thus presents the direct impression and typical futuristic experience of interaction interface of vehicles. The paper also discusses the human’s natural behavior while driving and related future technology advancement is developing, and the driving environment which is the most important topic for the whole industry. The paper analyzes the interaction requirements of automated driving vehicles and summarizes the development trend of the human-machine interaction system for automated driving, which results in better understanding of interaction interface of vehicles.
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Ji, Yong Gang, Han Ming Zheng, and Yan Peng Zhang. "Electromagnetic Identification Intelligent Vehicle System Design." Applied Mechanics and Materials 401-403 (September 2013): 1695–98. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1695.
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This paper introduces the electromagnetic identification intelligent vehicles hardware system design diagram and software design process based MC9S12XSl28 microcontroller, focusing on the design of intelligent vehicle power unit circuit, the electromagnetic signal amplification circuit, the motor drive module, gives a detailed schematic. Software design give a specific design flow, integrated hardware and software design constitutes intelligent vehicles overall system.
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Song, Xiulan, Xiaoxin Lou, Junwei Zhu, and Defeng He. "Secure State Estimation for Motion Monitoring of Intelligent Connected Vehicle Systems." Sensors 20, no. 5 (February 25, 2020): 1253. http://dx.doi.org/10.3390/s20051253.
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This paper considers the state estimation problem of intelligent connected vehicle systems under the false data injection attack in wireless monitoring networks. We propose a new secure state estimation method to reconstruct the motion states of the connected vehicles equipped with cooperative adaptive cruise control (CACC) systems. First, the set of CACC models combined with Proportion-Differentiation (PD) controllers are used to represent the longitudinal dynamics of the intelligent connected vehicle systems. Then the notion of sparseness is employed to model the false data injection attack of the wireless networks of the monitoring platform. According to the corrupted data of the vehicles’ states, the compressed sensing principle is used to describe the secure state estimation problem of the connected vehicles. Moreover, the L1 norm optimization problem is solved to reconstruct the motion states of the vehicles based on the orthogonaldecomposition. Finally, the simulation experiments verify that the proposed method can effectively reconstruct the motion states of vehicles for remote monitoring of the intelligent connected vehicle system.
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Brown, Alan S. "Intelligent Safety." Mechanical Engineering 129, no. 12 (December 1, 2007): 35–38. http://dx.doi.org/10.1115/1.2007-dec-3.
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The US Department of Transportation announced that it would go beyond active and passive safety systems to mandate the first use of a truly intelligent safety system. The new standard requires automakers to equip all vehicles with electronic stability control, which automatically brakes individual wheels during skids, by September 1, 2011. According to a senior staff member, electronic stability control is probably the most significant automotive safety technology since the seat belt. Electronic stability control combines sophisticated sensors and high-octane computing to take intelligent brake control to an entirely new level. Ford Motor Co. takes Electronic steering control (ESC) one step further with roll stability control, which senses when a van or SUV begins to tilt during a turn or emergency manoeuvre. It automatically takes countermeasures to prevent the vehicle from rolling over. Code-making organizations are currently developing broadcast and message standards for such systems, but it will take many vehicles with communications capacity to make them effective.
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Zhang, Mingyang, Heyan Xu, Ning Ma, and Xinglin Pan. "Intelligent Vehicle Sales Prediction Based on Online Public Opinion and Online Search Index." Sustainability 14, no. 16 (August 19, 2022): 10344. http://dx.doi.org/10.3390/su141610344.
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Intelligent vehicles refer to a new generation of vehicles with automatic driving functions that is gradually becoming an intelligent mobile space and application terminal by carrying advanced sensors and other devices and using new technologies, such as artificial intelligence. Firstly, the traditional autoregressive intelligent vehicle sales prediction model based on historical sales is established. Secondly, the public opinion data and online search index data are selected to establish a sales prediction model based on online public opinion and online search index. Then, we consider the influence of KOL (Key Opinion Leader), a sales prediction model based on KOL online public opinion andonline search index is established. Finally, the model is further optimized by using the deep learning algorithm LSTM (Long Short-Term Memory network), and the LSTM sales prediction model based on KOL online public opinion and online search index is established. The results show that the consideration of the online public opinion and search index can improve the prediction accuracy of intelligent vehicle sales, and the public opinion of KOL plays a greater role in improving the prediction accuracy of sales than that of the general public. Deep learning algorithms can further improve the prediction accuracy of intelligent vehicle sales.
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Shu, Ruizhi, Hang Gong, Guanghui Hu, and Jin Huang. "A Novel Intelligent Fan Clutch for Large Hybrid Vehicles." Energies 15, no. 12 (June 12, 2022): 4308. http://dx.doi.org/10.3390/en15124308.
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To solve the problems of complex structure, poor reliability, and low intelligence of existing fan clutches for large hybrid vehicles, this paper proposes a new adaptive shape memory alloy intelligent fan clutch for large hybrid vehicle motor cooling. Based on the pure shear shape memory alloy thermodynamic effects, the relationship between shape memory alloy spring recovery force and temperature has been established; based on the shape memory alloy spring thermal drive characteristics and clutch construction dimensions, clutch torque transmission equations have been established. The shape memory alloy fan clutch transmission characteristics were quantitatively analyzed in terms of temperature, torque, rotational speed, and slip rate. The results show that the shape memory alloy fan clutch model based on the finite element method (FEM) and the established transmission model can accurately describe the mechanical characteristics of the shape memory alloy phase change process and the clutch torque transmission characteristics. When the clutch input speed is 3000 rad/min and the temperature is 100 °C, the output torque is 19.04 N·m, the speed is 2877.2 rad/min, and the slip rate is 4.3%. Due to the shape memory effect of shape memory alloy, the clutch can intelligently adjust the fan speed by sensing the ambient temperature. A fan clutch can satisfy the heat dissipation requirement of a large hybrid vehicle’s transmission system under complicated road conditions.
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Wang, Zhi, Liguo Zang, Yiming Tang, Yehui Shen, and Zhenxuan Wu. "An Intelligent Networked Car-Hailing System Based on the Multi Sensor Fusion and UWB Positioning Technology under Complex Scenes Condition." World Electric Vehicle Journal 12, no. 3 (August 27, 2021): 135. http://dx.doi.org/10.3390/wevj12030135.
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In order to solve the problems of difficulty and long times to pick up cars in complex traffic scenes, this paper proposes an intelligent networked car-hailing system in complex scenes based on multi sensor fusion and Ultra-Wide-Band (UWB) technology. UWB positioning technology is adopted in the system, and the positioning data is optimized by the untraceable Kalman filter algorithm. Based on the environment perception technology of multi sensor fusion, such as machine vision and laser radar technology, an anti-collision warning algorithm was proposed in the process of car-hailing, which improved the safety factor of car-hailing. When the owner enters the parking lot, the intelligent vehicle can automatically locate the owner’s position and drive to the owner without human intervention, which provides a new idea for the development of intelligent networked vehicles and effectively improves the navigation accuracy and intelligence of intelligent vehicles.
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Tan, Lingling, and Bo Dong. "Research on the Development of "Vehicle-Road-Cloud Integration"." International Journal of Mechanical and Electrical Engineering 3, no. 1 (July 31, 2024): 52–57. http://dx.doi.org/10.62051/ijmee.v3n1.08.
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"Vehicle-Road-Cloud Integration" is key to solving the industrial development of intelligent connected vehicles and is also an important enabling technology for smart transportation. On July 3, 2024, the Ministry of Industry and Information Technology and four other departments released the list of pilot cities for the application of "Vehicle-Road-Cloud Integration" for intelligent connected vehicles, identifying 20 cities (alliances) as the first batch of pilot cities for "Vehicle-Road-Cloud Integration." The announcement of the pilot application list will accelerate the transition of intelligent driving from small-scale testing to large-scale implementation, further speeding up the widespread application and commercialization of advanced intelligent driving.
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Dushkin, Roman. "Multi-agent systems for cooperative ITS." Тренды и управление, no. 1 (January 2021): 42–50. http://dx.doi.org/10.7256/2454-0730.2021.1.34169.
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This article presents an original perspective upon the problem of creating intelligent transport systems in the conditions of using highly automated vehicles that freely move on the urban street-road networks. The author explores the issues of organizing a multi-agent system from such vehicles for solving the higher level tasks rather than by an individual agent (in this case – by a vehicle). Attention is also given to different types of interaction between the vehicles or vehicles and other agents. The examples of new tasks, in which the arrangement of such interaction would play a crucial role, are described. The scientific novelty is based on the application of particular methods and technologies of the multi-agent systems theory from the field of artificial intelligence to the creation of intelligent transport systems and organizing free-flow movement of highly automated vehicles. It is demonstrated the multi-agent systems are able to solve more complex tasks than separate agents or a group of non-interacting agents. This allows obtaining the emergent effects of the so-called swarm intelligence of the multiple interacting agents. This article may be valuable to everyone interested in the future of the transport sector.
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Kuang, Yunxin, and Yuling Liu. "Analysis and Design of the Smart Park Entry Logistics Vehicle Management System." Journal of Engineering System 1, no. 2 (June 2023): 40–47. http://dx.doi.org/10.62517/jes.202302208.
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With the rapid development and widespread application of information technology, 5G, cloud computing, artificial intelligence, and the Internet of Things (IoT), the construction of "smart parks" has become a common trend in park development. To create a "smart security" park with core features of security, access control, and energy efficiency, addressing the challenges of difficult monitoring, lack of order, and dispersed timing in the delivery process of inbound logistics vehicles, this study proposes an Intelligent Entry Logistics Vehicle Management System based on IoT technology. This system integrates wireless communication technology, mobile terminal technology, GPS positioning, and vehicle management application services from the Smart Park Data Service Platform to efficiently monitor logistics vehicles within the park. Furthermore, the system focuses on intelligent parking space planning and allocation to provide comprehensive support, including optimizing the processes of vehicle entry and exit and parking. The design goal of this system is to enhance the security of inbound logistics vehicles within the park and provide more convenient parking and cargo handling services.