Journal articles: 'Intelligent cruise control'

1

Ioannou, P. A., and C. C. Chien. "Autonomous intelligent cruise control." IEEE Transactions on Vehicular Technology 42, no. 4 (1993): 657–72. http://dx.doi.org/10.1109/25.260745.

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2

Björnberg, A. "Control Design for Autonomous Intelligent Cruise Control." IFAC Proceedings Volumes 27, no. 12 (August 1994): 835–40. http://dx.doi.org/10.1016/s1474-6670(17)47576-8.

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3

Palmquist, U. "Intelligent cruise control and roadside information." IEEE Micro 13, no. 1 (February 1993): 20–28. http://dx.doi.org/10.1109/40.210522.

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Song, Gui Qiu, Ying Yang, Haiqiang Hang, and Shu Hong Wang. "Study on Vehicle Collision-Avoiding Radar and Intelligent Cruise Control System." Key Engineering Materials 297-300 (November 2005): 311–15. http://dx.doi.org/10.4028/www.scientific.net/kem.297-300.311.

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An integrated vehicle collision-avoiding radar and intelligent cruise control system is proposed. Collision-avoiding radar measures the distance of a vehicle-to-vehicle and roadblocks automatically, and then Cruise Control System design optimal acceleration for the vehicle-to-vehicle distance control. An integrated radar and intelligent cruise control law has been proposed. Using this control law, the brake controller forces the vehicle acceleration to converge to the desired acceleration. It has been shown via the simulations with good distance control performance in both high speed and low speed stop and good driving situations. Vehicle Collision-avoiding Radar System and Intelligent Cruise Control System have very important significance on improving vehicle active safety and reducing driver’s fatigue. Collision-avoiding Radar System and Intelligent Cruise Control System will be the necessary equipment in future vehicle.

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Bian, Chentong, Guodong Yin, Liwei Xu, and Ning Zhang. "Bidirectional adaptive cruise control for intelligent vehicles." International Journal of Heavy Vehicle Systems 28, no. 4 (2021): 467. http://dx.doi.org/10.1504/ijhvs.2021.118238.

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Zhang, Ning, Liwei Xu, Guodong Yin, and Chentong Bian. "Bidirectional adaptive cruise control for intelligent vehicles." International Journal of Heavy Vehicle Systems 28, no. 4 (2021): 467. http://dx.doi.org/10.1504/ijhvs.2021.10041913.

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Zhang, Xiwen, and Thomas Benz. "SIMULATION AND EVALUATION OF “INTELLIGENT CRUISE CONTROL”." I V H S Journal 1, no. 2 (January 1993): 181–90. http://dx.doi.org/10.1080/10248079308903791.

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Han, Yong Qi, Li Ying Cao, and Chun Guang Bi. "Research of an Automatic Cruise Intelligent Vehicle Control Program." Advanced Materials Research 1049-1050 (October 2014): 1030–32. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1030.

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Control core of Intelligent automatic patrol car track (photovoltaic group) uses G128 chip, which function modules include: laser sensor, speed control module, the servo control module. The basic principle is that the control center sends a signal to the laser sensor module, which controls laser tube launching and receives data by laser receiver returning. According to the data of the receiver, the core module synchronously controls servo module and the speed module. The main program uses a PID algorithm by C language.

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Minderhoud, Michiel M., and Piet H. L. Bovy. "Impact of Intelligent Cruise Control on Motorway Capacity." Transportation Research Record: Journal of the Transportation Research Board 1679, no. 1 (January 1999): 1–9. http://dx.doi.org/10.3141/1679-01.

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Darbha, Swaroop, and K. R. Rajagopal. "Intelligent cruise control systems and traffic flow stability." Transportation Research Part C: Emerging Technologies 7, no. 6 (December 1999): 329–52. http://dx.doi.org/10.1016/s0968-090x(99)00024-8.

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Chira-Chavala, T., and S. M. Yoo. "Potential safety benefits of intelligent cruise control systems." Accident Analysis & Prevention 26, no. 2 (April 1994): 135–46. http://dx.doi.org/10.1016/0001-4575(94)90083-3.

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12

Girard, A. R., S. Spry, and J. K. Hedrick. "Intelligent cruise-control applications - Real-time, embedded hybrid control software." IEEE Robotics & Automation Magazine 12, no. 1 (March 2005): 22–28. http://dx.doi.org/10.1109/mra.2005.1411415.

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Dellnitz, Michael, Julian Eckstein, Kathrin Flaßkamp, Patrick Friedel, Christian Horenkamp, Ulrich Köhler, Sina Ober-Blöbaum, Sebastian Peitz, and Sebastian Tiemeyer. "Development of an Intelligent Cruise Control Using Optimal Control Methods." Procedia Technology 15 (2014): 285–94. http://dx.doi.org/10.1016/j.protcy.2014.09.082.

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14

Hager, R., R. Mathar, and J. Mattfeldt. "Intelligent cruise control and reliable communication of mobile stations." IEEE Transactions on Vehicular Technology 44, no. 3 (1995): 443–48. http://dx.doi.org/10.1109/25.406610.

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Chen, Xue-wen, Jin-guo Zhang, and Yan-jun Liu. "Research on the Intelligent Control and Simulation of Automobile Cruise System Based on Fuzzy System." Mathematical Problems in Engineering 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/9760653.

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In order to improve the active safety driving vehicle and alleviate the intension of driving fatigue, an intelligent control strategy of automobile cruise is put forward based on the throttle or braking pedal combined control adopting the fuzzy control theory. A fuzzy logic controller is presented, which consists of the two input variables, the deviation of the theoretical safe distance and relative distance and the relative velocity between the preceding vehicle and the cruise vehicle, and the single output variable, that is, the throttle opening or the braking pedal travel. Taking the test data of 1.6 L vehicle with auto-transmission as an example, the function on the intelligent cruise control system is simulated adopting MATLAB/Simulink aiming at different working conditions on the city road. The simulation results show that the control strategy possesses integrated capability of automated Stop & Go control, actively following the preceding vehicle on the conditions of keeping the safety distance and the constant velocity cruise. The research results can offer the theory and technology reference for setting dSPACE type and developing the integrated control product of automobile cruise system.

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Ow, HuiNee, Muataz H. Salih, and ChinBeng Lim. "Design and implementation of laser based intelligent embedded dual modes cruise control system using FPGA." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 3 (March 1, 2019): 1014. http://dx.doi.org/10.11591/ijeecs.v13.i3.pp1014-1021.

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<span>Cruise control system is also known as speed control which use to take over the car from the driver and stabilize the car with the speed had been set. It is one of the important elements in the intelligent vehicles. Currently, driver has to accelerate the car until certain speed in order to activate cruise control system. After it had been activated, driver still needs to put on attention to decelerate the car when there is another one in front and possibility to get accident is high. To overcome this problem and enhance current cruise system, this project has two laser-based working modes. First one is normal mode that following the same current cruise system procedure. Second is pre-set mode that allow driver to enter required speed without necessity to use petrol pedal. Both modes will use laser transceivers to add on perception feature to cruise control system. The system is designed and implemented using FPGA as main processing technology. Also, Quartus II 13.0 SP is used and DE0-Nano board is utilized as project platform. The input management is implemented to handle all sensors data and feed main processing unit with proper data stream. The result is efficient in term of system response, accurate cruise control and trustable design as well. The implement system is achieved up to 1.3GHz and 1,337 logic elements are consumed.</span>

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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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Bose, Arnab, and Petros Ioannou. "Evaluation of the Environmental Effects of Intelligent Cruise Control Vehicles." Transportation Research Record: Journal of the Transportation Research Board 1774, no. 1 (January 2001): 90–97. http://dx.doi.org/10.3141/1774-11.

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LUO, Yugong. "Adaptive Cruise Control System of Besturn Intelligent Hybrid Electric Vehicle." Journal of Mechanical Engineering 46, no. 06 (2010): 2. http://dx.doi.org/10.3901/jme.2010.06.002.

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20

Yi, K., S. Lee, and Y. D. Kwon. "An investigation of intelligent cruise control laws for passenger vehicles." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 215, no. 2 (February 1, 2001): 159–69. http://dx.doi.org/10.1243/0954407011525502.

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This paper describes intelligent cruise control (ICC) laws for passenger vehicles. ICC systems consist of a vehicle detection sensor, a controller and throttle/brake actuators. For the control of a throttle/brake system, a solenoid valve controlled electronic vacuum booster (EVB) and a step motor controlled throttle actuator have been used. A non-linear computer model for the electronic vacuum booster has been developed and the simulations were performed using a complete non-linear vehicle model. The proposed control law in this paper consists of an algorithm that generates the desired acceleration/deceleration profile in an ICC situation, a throttle/brake switching logic and a throttle/brake control algorithm. The control performance has been investigated through computer simulations and vehicle tests. The test vehicle is equipped with a millimetre wave radar distance sensor, an Intel 80C196 controller, a solenoid valve controlled EVB and a step motor controlled throttle actuator. The results indicate that the proposed throttle/brake control laws can provide satisfactory vehicle-to-vehicle distance and velocity control performance.

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Vahidi, A., and A. Eskandarian. "Research advances in intelligent collision avoidance and adaptive cruise control." IEEE Transactions on Intelligent Transportation Systems 4, no. 3 (September 2003): 143–53. http://dx.doi.org/10.1109/tits.2003.821292.

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Russell, M. E., A. Crain, A. Curran, R. A. Campbell, C. A. Drubin, and W. F. Miccioli. "Millimeter-wave radar sensor for automotive intelligent cruise control (ICC)." IEEE Transactions on Microwave Theory and Techniques 45, no. 12 (1997): 2444–53. http://dx.doi.org/10.1109/22.643858.

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23

Somda, F. H., and H. Cormerais. "Auto-adaptive and string stable strategy for intelligent cruise control." IET Intelligent Transport Systems 5, no. 3 (2011): 168. http://dx.doi.org/10.1049/iet-its.2010.0016.

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24

Basargan, Hakan, András Mihály, Péter Gáspár, and Olivier Sename. "Intelligent Road-Adaptive Semi-Active Suspension and Integrated Cruise Control." Machines 11, no. 2 (February 1, 2023): 204. http://dx.doi.org/10.3390/machines11020204.

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The availability of road and vehicle data enables the control of road vehicles to adapt for different road irregularities. Vision-based or stored road data inform the vehicle regarding the road ahead and surface conditions. Due to these abilities, the vehicle can be controlled efficiently to deal with different road irregularities in order to improve driving comfort and stability performances. The present paper proposes an integration method for an intelligent, road-adaptive, semi-active suspension control and cruise control system. The road-adaptive, semi-active suspension controller is designed through the linear parameter-varying (LPV) method, and road adaptation is performed with a road adaptivity algorithm that considers road irregularities and vehicle velocity. The road adaptivity algorithm calculates a dedicated scheduling variable that modifies the operating mode of the LPV controller. This modification of operation mode provides a trade-off between driving comfort and vehicle stability performances. Regarding the cruise control, the velocity design of the vehicle is based on the ISO 2631-1 standard, the created database, and the look-ahead road information. For each road irregularity, the velocity of the vehicle is designed according to previous measurements and the table of ISO 2631-1 standard. The comfort level must be selected in order to calculate dedicated velocity for road irregularity. The designed velocity is tracked by the velocity-tracking controller evaluated with the LPV control framework. The designed controllers are integrated, and the operation of the integrated method is validated in a TruckSim simulation environment.

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25

Li, Zhaobo, Yimin Deng, and Shuanglei Sun. "Adaptive Cruise Predictive Control Based on Variable Compass Operator Pigeon-Inspired Optimization." Electronics 11, no. 9 (April 26, 2022): 1377. http://dx.doi.org/10.3390/electronics11091377.

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A vehicle adaptive cruise system can control the speed and the safe distance between vehicles rapidly and effectively, which is an integral part of an intelligent driver assistance system. Adaptive cruise predictive control algorithms based on variable compass operator pigeon-inspired optimization (PIO) and PSO are proposed to improve the time response characteristics of multi-objective adaptive cruise system predictive control. Firstly, a longitudinal kinematic model of an adaptive cruise system was established and linearly discretized. Secondly, the multi-objective optimal cost function and parameter constraints were designed by integrating factors such as distance error, relative speed, acceleration and impact, and a mathematical model of the adaptive cruise predictive control optimization problem was constructed. Finally, PIO and PSO were used to solve the optimal control law for MPC and simulated by Matlab. The results show that the adaptive cruise system can reach a steady state quickly with the control laws of PIO or PSO. However, due to the global optimization and fast convergence characteristic, variable compass operator PIO has better time response characteristics.

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Li, Zhaobo, Yimin Deng, and Shuanglei Sun. "Adaptive Cruise Predictive Control Based on Variable Compass Operator Pigeon-Inspired Optimization." Electronics 11, no. 9 (April 26, 2022): 1377. http://dx.doi.org/10.3390/electronics11091377.

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A vehicle adaptive cruise system can control the speed and the safe distance between vehicles rapidly and effectively, which is an integral part of an intelligent driver assistance system. Adaptive cruise predictive control algorithms based on variable compass operator pigeon-inspired optimization (PIO) and PSO are proposed to improve the time response characteristics of multi-objective adaptive cruise system predictive control. Firstly, a longitudinal kinematic model of an adaptive cruise system was established and linearly discretized. Secondly, the multi-objective optimal cost function and parameter constraints were designed by integrating factors such as distance error, relative speed, acceleration and impact, and a mathematical model of the adaptive cruise predictive control optimization problem was constructed. Finally, PIO and PSO were used to solve the optimal control law for MPC and simulated by Matlab. The results show that the adaptive cruise system can reach a steady state quickly with the control laws of PIO or PSO. However, due to the global optimization and fast convergence characteristic, variable compass operator PIO has better time response characteristics.

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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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Gilling, Simon P. "Collision Avoidance, Driver Support and Safety Intervention Systems." Journal of Navigation 50, no. 1 (January 1997): 27–32. http://dx.doi.org/10.1017/s0373463300023559.

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Autonomous Intelligent Cruise Control (AICC) will be marketed by a number of vehicle manufacturers before the end of the decade. This paper will describe AICC and the next generation systems currently being developed and validated within the EC Fourth Framework project, Anti-Collision Autonomous Support and Safety Intervention SysTem (AC ASSIST).The currently available cruise control systems which maintain a fixed speed are a well-known form of longitudinal driver support. The fixed speed cruise control becomes less useful with increased traffic volumes, as the driver must disable the system when a slower preceding vehicle is encountered.

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Wang, Long Sheng, Hong Ze Xu, and Heng Yu Luo. "An Intelligent Cruise Controller for High-Speed Train Operation Based on Fuzzy Neural Network Theory." Applied Mechanics and Materials 300-301 (February 2013): 1405–11. http://dx.doi.org/10.4028/www.scientific.net/amm.300-301.1405.

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An intelligent control strategy is proposed in this paper, which is applied to the high-speed train ATO (Automatic Train Operation) system in the cruise condition. The dynamics of a high-speed train is discussed based on a typical single-point-mass model and the force analysis in cruise state is studied. A fuzzy neural network control algorithm is incorporated into the ATO system aiming at improving the velocity and position tracking performance in the cruise operation of high-speed train. This control scheme adjusts the parameters of membership functions on-line and does not rely on the precise system parameters such as resistance coefficients which are very difficult to measure in practice. The numerical simulation verifies the effectiveness of this fuzzy neural network algorithm.

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SWAROOP, D., and R. HUANDRA. "Intelligent Cruise Control System Design based on a Traffic Flow Specification." Vehicle System Dynamics 30, no. 5 (November 1998): 319–44. http://dx.doi.org/10.1080/00423119808969455.

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31

Touran, Ali, Mark A. Brackstone, and Mike McDonald. "A collision model for safety evaluation of autonomous intelligent cruise control." Accident Analysis & Prevention 31, no. 5 (September 1999): 567–78. http://dx.doi.org/10.1016/s0001-4575(99)00013-5.

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32

HOSHINO, Satoshi, Hiroya SEKI, Yuji NAKA, and Jun OTA. "3312 Intelligent Cruise Control for Transport Robots in Flexible Manufacturing Systems." Proceedings of the Transportation and Logistics Conference 2009.18 (2009): 339–42. http://dx.doi.org/10.1299/jsmetld.2009.18.339.

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33

Álvarez, S., M. Á. Sotelo, M. Ocaña, D. F. Llorca, I. Parra, and L. M. Bergasa. "Perception advances in outdoor vehicle detection for automatic cruise control." Robotica 28, no. 5 (September 4, 2009): 765–79. http://dx.doi.org/10.1017/s0263574709990464.

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SUMMARYThis paper describes a vehicle detection system based on support vector machine (SVM) and monocular vision. The final goal is to provide vehicle-to-vehicle time gap for automatic cruise control (ACC) applications in the framework of intelligent transportation systems (ITS). The challenge is to use a single camera as input, in order to achieve a low cost final system that meets the requirements needed to undertake serial production in automotive industry. The basic feature of the detected objects are first located in the image using vision and then combined with a SVM-based classifier. An intelligent learning approach is proposed in order to better deal with objects variability, illumination conditions, partial occlusions and rotations. A large database containing thousands of object examples extracted from real road scenes has been created for learning purposes. The classifier is trained using SVM in order to be able to classify vehicles, including trucks. In addition, the vehicle detection system described in this paper provides early detection of passing cars and assigns lane to target vehicles. In the paper, we present and discuss the results achieved up to date in real traffic conditions.

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Chen, Xue-Wen, Yue Zhou, Jin-guo Zhang, and Zhi-feng Wang. "A synergetic strategy of automobile intelligent cruise system based on fuzzy control adopting hierarchical structure." International Journal of Advanced Robotic Systems 16, no. 5 (September 1, 2019): 172988141987775. http://dx.doi.org/10.1177/1729881419877758.

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To improve automobile travel safety and relieve the intension of driving fatigue, a synergetic strategy on the throttle and brake combined control of automobile cruise system is proposed based on fuzzy control theory. A hierarchical control structure, including the upper fuzzy controller and the lower model match controller, is designed for integrated control function. The upper controller consists of the two input variables, that is, the deviation of the theoretical safe distance and the relative distance and the relative velocity between the host car and the preceding vehicle, and the single output variable, that is, the excepted acceleration of host car. The lower controller has achieved the fast compensation of excepted acceleration by adopting parallel feedforward and feedback compensator to input into the inverse dynamics model of automobile cruise system. Using MATLAB/Simulink, the control function on the proposed synergetic strategy is validated for different working cases. The results show that the synergetic strategy possesses an integrated capability involved automated stop & go control, and following the preceding car to prevent rear-end collision, and the constant velocity cruise whether the driving conditions of the city road or expressway.

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Mo, Hong, Yinghui Meng, Fei-Yue Wang, and Dongrui Wu. "Interval Type-2 Fuzzy Hierarchical Adaptive Cruise Following-Control for Intelligent Vehicles." IEEE/CAA Journal of Automatica Sinica 9, no. 9 (September 2022): 1658–72. http://dx.doi.org/10.1109/jas.2022.105806.

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Hu, Bo, Jiaxi Li, Jie Yang, Haitao Bai, Shuang Li, Youchang Sun, and Xiaoyu Yang. "Reinforcement Learning Approach to Design Practical Adaptive Control for a Small-Scale Intelligent Vehicle." Symmetry 11, no. 9 (September 7, 2019): 1139. http://dx.doi.org/10.3390/sym11091139.

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Reinforcement learning (RL) based techniques have been employed for the tracking and adaptive cruise control of a small-scale vehicle with the aim to transfer the obtained knowledge to a full-scale intelligent vehicle in the near future. Unlike most other control techniques, the purpose of this study is to seek a practical method that enables the vehicle, in the real environment and in real time, to learn the control behavior on its own while adapting to the changing circumstances. In this context, it is necessary to design an algorithm that symmetrically considers both time efficiency and accuracy. Meanwhile, in order to realize adaptive cruise control specifically, a set of symmetrical control actions consisting of steering angle and vehicle speed needs to be optimized simultaneously. In this paper, firstly, the experimental setup of the small-scale intelligent vehicle is introduced. Subsequently, three model-free RL algorithm are conducted to develop and finally form the strategy to keep the vehicle within its lanes at constant and top velocity. Furthermore, a model-based RL strategy is compared that incorporates learning from real experience and planning from simulated experience. Finally, a Q-learning based adaptive cruise control strategy is intermixed to the existing tracking control architecture to allow the vehicle slow-down in the curve and accelerate on straightaways. The experimental results show that the Q-learning and Sarsa (λ) algorithms can achieve a better tracking behavior than the conventional Sarsa, and Q-learning outperform Sarsa (λ) in terms of computational complexity. The Dyna-Q method performs similarly with the Sarsa (λ) algorithms, but with a significant reduction of computational time. Compared with a fine-tuned proportion integration differentiation (PID) controller, the good-balanced Q-learning is seen to perform better and it can also be easily applied to control problems with over one control actions.

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Liu, Di, Yunfeng Hu, Jinghua Zhao, Yao Sun, and Hong Chen. "Eco-adaptive cruise control of diesel commercial vehicle in the intelligent network environment." IFAC-PapersOnLine 54, no. 10 (2021): 271–77. http://dx.doi.org/10.1016/j.ifacol.2021.10.175.

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Bae, Jong-Il. "An Adaptive Cruise Control Systems for Intelligent Vehicles in Accordance with Vehicles Distance." Transactions of The Korean Institute of Electrical Engineers 62, no. 8 (August 1, 2013): 1157–62. http://dx.doi.org/10.5370/kiee.2013.62.8.1157.

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HOSHINO, Satoshi, Hiroya SEKI, Yuji NAKA, and Jun OTA. "Intelligent Cruise Control of Circulating Multi-Robot Effective for Congestion(TRANSLOG2009/J-RAIL2009)." Transactions of the Japan Society of Mechanical Engineers Series C 76, no. 770 (2010): 2387–95. http://dx.doi.org/10.1299/kikaic.76.2387.

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Zhu, Guoming G., and Chengsheng Miao. "Real-Time Co-optimization of Vehicle Route and Speed Using Generic Algorithm for Improved Fuel Economy." Mechanical Engineering 141, no. 03 (March 1, 2019): S08—S15. http://dx.doi.org/10.1115/1.2019-mar-4.

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Making future vehicles intelligent with improved fuel economy and satisfactory emissions are the main drivers for current vehicle research and development. The connected and autonomous vehicles still need years or decades to be widely used in practice. However, some advanced technologies have been developed and deployed for the conventional vehicles to improve the vehicle performance and safety, such as adaptive cruise control (ACC), automatic parking, automatic lane keeping, active safety, super cruise, and so on. On the other hand, the vehicle propulsion system technologies, such as clean and high efficiency combustion, hybrid electric vehicle (HEV), and electric vehicle, are continuously advancing to improve fuel economy with satisfactory emissions for traditional internal combustion engine powered and hybrid electric vehicles or to increase cruise range for electric vehicles.

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Noor, Ahmed K., and Sven A. Beiker. "Intelligent and Connected." Mechanical Engineering 134, no. 11 (November 1, 2012): 32–37. http://dx.doi.org/10.1115/1.2012-nov-2.

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This article reviews the research and development of automated connected vehicles that aim to reduce road accidents, money, fuel, and conserve environment. Major automotive companies have added automated functions to their vehicles, and various driver assistance systems—adaptive cruise control, video-based lane analysis, and steering and braking assistance—are currently available on high-end models. Automated systems can assess some traffic situations faster than humans can. As a result, automated driving is expected to significantly reduce accidents and traffic fatalities, improve traffic flow and highway capacity, achieve better fuel efficiency, and reduce emissions. However, on the way towards fully automated driving, many challenges need to be addressed. There are technology issues, including reliability, and non-technical issues of cost, regulation, and legislation. In order to accelerate the development of fully automated connected vehicles, there is a need for a cooperative approach. A practical evolutionary roadmap can be developed by an interdisciplinary panel of experts representing major car companies, government agencies, research centers, and academia.

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Khronusova, Tatiana Valerievna, Askhat Zamilovich Asanov, and Maksim Anatolievich Nazarenko. "On-board information and control systems that automate the movement of vehicles in a column using the example of heavy vehicles." Кибернетика и программирование, no. 2 (February 2019): 30–43. http://dx.doi.org/10.25136/2306-4196.2019.2.21490.

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The subject of this article is on-board information control systems that ensure the movement of heavy vehicles in a convoy with the driver only in the first car, as well as cruise control systems, including adaptive and intelligent ones. The purpose of the article is the formation of the specified on-board system of a heavy truck, which includes both a set of algorithms and key functions that implement movement in a column, and a list of used categories of sensors. All considered functions are arranged hierarchically at three levels: strategic, tactical and operational. The study collected both theoretical and practical information provided by automakers, revealing the essential content of onboard information systems and cruise control. The novelty of the study lies in the fact that the model of intellectual cruise control has been improved, providing movement of heavy vehicles in a convoy, if there is a driver only in the first car. The proposed model includes the hierarchical architecture of the on-board information management system, key algorithms and sensors for its implementation.

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Zhao, Dongbin, Zhongpu Xia, and Qichao Zhang. "Model-Free Optimal Control Based Intelligent Cruise Control with Hardware-in-the-Loop Demonstration [Research Frontier]." IEEE Computational Intelligence Magazine 12, no. 2 (May 2017): 56–69. http://dx.doi.org/10.1109/mci.2017.2670463.

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Washino, Shoichi. "Intelligent Transport Systems. Human Factors & other related factors in Adaptive Cruise Control System." Journal of the Robotics Society of Japan 17, no. 3 (1999): 345–47. http://dx.doi.org/10.7210/jrsj.17.345.

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Van AERDE, M., and H. RAKHA. "A Framework for the Evaluation of System Safety Benefits of Intelligent Cruise Control Systems." ITS Journal - Intelligent Transportation Systems Journal 5, no. 2 (January 1999): 163–89. http://dx.doi.org/10.1080/10248079908903763.

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46

Kwon, Tae-Wook, and Yoon-Chul Choy. "Intelligent Cruise-Control Navigation: A new navigation/travel method for use in virtual environments." Virtual Reality 5, no. 1 (March 2000): 23–31. http://dx.doi.org/10.1007/bf01418973.

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47

Xie, Hui, and Pengbo Xiao. "Cooperative Adaptive Cruise Algorithm Based on Trajectory Prediction for Driverless Buses." Machines 10, no. 10 (October 3, 2022): 893. http://dx.doi.org/10.3390/machines10100893.

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Cooperative adaptive cruise control (CACC) technology offers a proven solution to the current traffic congestion problems caused by the yearly growth of car ownership. Coping with random lane changes of bypass vehicles under the condition of traffic congestion is a challenge for urban driverless vehicles. In this paper, to meet the demand for high comfort driverless buses driving on urban roads, an active anti-disturbance following control method for driverless buses based on bystander vehicle intention recognition and trajectory prediction is proposed for the scenario of bystander vehicle cut-in during driving to alleviate the disturbance caused by bystander vehicles, improve passenger comfort, and suppress multi-vehicle oscillation. The simulation results show that the intelligent prediction system-based queue reduces the traffic oscillation rate by an average of 9.8% and improves the comfort level by an average of 11% under side-car insertion conditions. The results of the real vehicle test show that the vehicles based on the intelligent prediction algorithm have a 25.5% reduction in maximum speed adjustment, 14.5 m average reduction in following distance, 6% improvement in comfort, and 27% improvement in rear vehicle comfort.

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Abdurohman, Abdurohman, He Xi Kang, and Taufik Hidayat. "Vehicle ACC Control Based on Fuzzy PID." International Journal of Engineering Continuity 1, no. 1 (December 10, 2022): 36–55. http://dx.doi.org/10.58291/ijec.v1i1.38.

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ACC (Adaptive Cruise Control) is a crucial technology to control the automatic driving of a vehicle, which can automatically adjust the driving status of the vehicle according to the driving status of the vehicle in front so that the vehicle always keeps a safe distance from the vehicle in front, which can primarily relieve the driver's driving pressure and avoid tailgating. The research is of great significance to the realization of intelligent driving of vehicles. In this paper, the fuzzy PID control method is used to study the vehicle ACC system. Firstly, the basic concept and key components of the ACC system are explained, the vehicle longitudinal dynamics model is established, and the primary ranges of , and parameters are determined through simulation; based on this, the fuzzy PID control algorithm of vehicle ACC is designed considering three different driving conditions of fixed speed and following cruise mode. The fuzzy PID control algorithm of the vehicle ACC system is designed and established. The joint simulation model of MATLAB is used to simulate the vehicle ACC system under three different driving conditions. The simulation results show that the vehicle ACC system designed in this paper ensures the safety and stability of following the vehicle while providing the vehicle's ride's comfort.

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Al-Saadi, Ziad, Duong Phan Van, Ali Moradi Amani, Mojgan Fayyazi, Samaneh Sadat Sajjadi, Dinh Ba Pham, Reza Jazar, and Hamid Khayyam. "Intelligent Driver Assistance and Energy Management Systems of Hybrid Electric Autonomous Vehicles." Sustainability 14, no. 15 (July 31, 2022): 9378. http://dx.doi.org/10.3390/su14159378.

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Automotive companies continue to develop integrated safety, sustainability, and reliability features that can help mitigate some of the most common driving risks associated with autonomous vehicles (AVs). Hybrid electric vehicles (HEVs) offer practical solutions to use control strategies to cut down fuel usage and emissions. AVs and HEVs are combined to take the advantages of each kind to solve the problem of wasting energy. This paper presents an intelligent driver assistance system, including adaptive cruise control (ACC) and an energy management system (EMS), for HEVs. Our proposed ACC determines the desired acceleration and safe distance with the lead car through a switched model predictive control (MPC) and a neuro-fuzzy (NF) system. The performance criteria of the switched MPC toggles between speed and distance control appropriately and its stability is mathematically proven. The EMS intelligently control the energy consumption based on ACC commands. The results show that the driving risk is extremely reduced by using ACC-MPC and ACC-NF, and the vehicle energy consumption by driver assistance system based on ACC-NF is improved by 2.6%.

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Gao, Tao. "Research on Simulation Algorithm of Series Hybrid Electric Vehicle Energy and Intelligent Control." International Journal of Advanced Pervasive and Ubiquitous Computing 9, no. 4 (October 2017): 33–77. http://dx.doi.org/10.4018/ijapuc.2017100103.

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Hybrid electric vehicle (HEV) is a kind of new cars with low fuel consumption and low emissions, which combines the advantages of traditional vehicle's long endurance and no-pollution of pure electric vehicles. It represents the future direction of development of vehicle for a period of time. Therefore, the research of HEV technology has important practical significance to the development of China's automobile. This paper takes Shijiazhuang bus as the research object, makes parameter matching according to the parameters of the vehicle, builds the vehicle model using Cruise software, set the simulation task, and studies the control strategy to reduce automobile fuel and pollutant emission targets. The research of this paper has certain directive significance to the modeling and energy optimization of hybrid electric vehicle.

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Journal articles on the topic 'Intelligent cruise control'

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