Adaptive Cruise Control System: A Literature Survey
محتوى المقالة الرئيسي
الملخص
Adaptive cruise control (ACC) assists automobiles in preserving a safe following distance and adhering to speed limits. This advanced driver-assistance system (ADAS) modifies the car's speed to keep a safe gap from oncoming traffic. All vehicle types include combustion engines, pure electric vehicles, hybrid electric vehicles, and methods of operation; controllers are designed to react to cruise control signals and provide an efficient route profile according to the surrounding environment and instantaneous vehicle performance characteristics. ACC uses a perception system to measure the forward vehicle's current distance, speed, and acceleration relative to the host vehicle. Some of these systems use lasers, radar, cameras, or a combination of these sensors to determine the distance and speed of the leading vehicle. Other systems even use wireless communication to collect data from surrounding vehicles. ACC can help reduce stress on long drives, increase road safety, prevent accidents, and enhance traffic flow energy efficiency. This paper aims to introduce a comprehensive study of the research on ACC and mention different controlling techniques used to deal with the problem. Furthermore, a discussion of each method with its cons and pros is mentioned too. First, an introduction to the ACC system and control approaches with a brief discussion of their main principle is presented. Next, various application cases of ACC are presented. These applications include lateral dynamics, wireless technology, energy vehicles, navigation data, and practical experimental tests. At last, future guidance and challenges are discussed.
تفاصيل المقالة
القسم
كيفية الاقتباس
المراجع
Abdulwahhab, O. W. and Abbas, N. H., 2017. A new analytic method to tune a fractional order PID controller, Journal of Engineering, 23(12), pp. 1–12. https://doi:10.31026/j.eng.2017.12.01.
Al-Mulla Hummadi, R. M.K., 2012. Simulation of optimal speed control for a DC motor using linear quadratic regulator (LQR), Journal of Engineering,18(03), pp.340 349. https://https://doi.org/10.31026/j.eng.2012.03.07.
Al-Neami, A. H., Alani, H. M. and Talabany, A. F.S, 2006. Study of the effect of vehicle driver behaviour on vehicle emissions of carbon monoxide at signalized intersections, Journal of Engineering, 12(01), pp. 71–84. https://https://doi.org/10.31026/j.eng.2006.01.06.
Bageshwar, V. L., Garrard, W. L. and Rajamani, R., 2004. Model predictive control of transitional maneuvers for adaptive cruise control vehicles," in IEEE Transactions on Vehicular Technology, 53(5), pp. 1573-1585, Sept. 2004, https://doi.org/10.1109/TVT.2004.833625.
Balaska, H., Ladaci, S., Schulte, H. and Djouambi, A., 2019. Adaptive cruise control system for an electric vehicle using a fractional order model reference adaptive strategy, IFAC-PapersOnLine, Volume 52, Issue 13, 2019, pp. 194-199, ISSN 2405-8963. https://doi.org/10.1016/j.ifacol.2019.11.096.
Bu, F. and Chan, CY., 2012. Adaptive and cooperative cruise control. In: Eskandarian, A. (eds) Handbook of Intelligent Vehicles. Springer, London. https://doi.org/10.1007/978-0-85729-085-4_9.
Cao, Z., Yang, D., Jiang, K., Wang, T., Jiao, X. and Xiao, Z. , 2017. End-to-end adaptive cruise control based on timing network. In: (SAE-China), S. (eds) Proceedings of the 19th Asia Pacific Automotive Engineering Conference & SAE-China Congress 2017: Selected Papers. SAE-China 2017. Lecture Notes in Electrical Engineering, vol 486. Springer, Singapore. https://doi.org/10.1007/978-981-10-8506-2_56.
Chamraz, S. and Balogh, R., 2018. Two approaches to the adaptive cruise control (ACC) design, 2018 Cybernetics & Informatics (K&I), Lazy pod Makytou, Slovakia, pp. 1-6, https://doi.org/10.1109/CYBERI.2018.8337542.
Chaturvedi, S. and Kumar, N. 2021. Design and Implementation of an Optimised PID Controller for the Adaptive Cruise Control System, IETE Journal of Research, https://doi.org/10.1080/03772063.2021.2012282.
Chu, H., Guo, L., Gao, B., Chen, H., Bian, N. and Zhou, J., 2018. Predictive cruise control using high-definition map and real vehicle implementation. IEEE Transactions on Vehicular Technology, 67(12), 11377-11389. Available from: https://doi.org/10.1109/TVT.2018.2871202.
Coskun, S., Cong, H. and Zhang, F., 2021. Quadratic programming-based cooperative adaptive cruise control under uncertainty via receding horizon strategy. Transactions of the Institute of Measurement and Control. 43. 014233122199274. https://doi.org/10.1177/0142331221992741.
David, J., Brom, P., Starý, F., Bradáč, J., and Dynybyl, V., 2021. Application of artificial neural networks to streamline the process of adaptive cruise control. Sustainability 13, no. 8: 4572. https://doi.org/10.3390/su13084572.
Feng, S., Zhao, Y., Deng, H. and Wang, Q., 2022. Binary search tree-based explicit MPC controller design with Kalman filter for vehicular adaptive cruise system. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. 2022;236(5):950-970. https://doi.org/10.1177/09544070211029780.
Fu, H., Liu, D., Wang, G. and Kamel, A.E., 2019. Integrated longitudinal and lateral control system design and case study on an electric vehicle. Mathematical Problems in Engineering Volume 2019, Article ID 3916917, 13 pages. https://doi.org/10.1155/2019/3916917.
Ganji, B., Kouzani, A. Z., Khoo, S. Y. and Nasir, M., 2014. A sliding-mode-control-based adaptive cruise controller. 11th IEEE International Conference on Control & Automation (ICCA), Taichung, Taiwan, 2014, pp. 394-397, https://doi.org/10.1109/ICCA.2014.6870952.
Ganji, B., Kouzani, A.Z., Khoo, S.Y. and Zahraei, M.S., 2014 Adaptive cruise control of a HEV using sliding mode control. Expert Systems with Applications, Volume 41, Issue 2, 2014, Pages 607-615, ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2013.07.085.
Gao, W., Gao, J., Ozbay, K. and Jiang, P., 2019. Reinforcement-learning-based cooperative adaptive cruise control of buses in the Lincoln tunnel corridor with time-varying topology. in IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 10, pp. 3796-3805, Oct. 2019, https://doi.org/10.1109/TITS.2019.2895285.
Gáspár, P. and Németh, B., 2014. Design of adaptive cruise control for road vehicles using topographic and traffic information. IFAC Proceedings Volumes, 47(3), 4184-4189. https://doi.org/10.3182/20140824-6-ZA-1003.01912.
Gonzalez, O. and Rossiter, J., 2020. Shifting strategy for efficient block-based nonlinear model predictive control using real-time iterations. IET Control Theory and Applications, 14 (6). pp. 865-877. https://doi.org/10.1049/iet-cta.2019.0369.
Goodall, N.J. and Lan, C.L., 2020. Car-following characteristics of adaptive cruise control from empirical data. Journal of Transportation Engineering, Part A: Systems, 146(9), p.04020097. https://doi.org/10.31224/osf.io/z8ktu.
Gunter, G., Stern, R. and Work, D.B., 2019, October. Modeling adaptive cruise control vehicles from experimental data: model comparison. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC) (pp. 3049-3054). IEEE. https://doi.org/10.1109/ITSC.2019.8917347.
Guo, L., Ge, P., Sun, D. and Qiao, Y., 2020. Adaptive cruise control based on model predictive control with constraints softening. Applied Sciences 10(5), pp. 1635. https://doi.org/10.3390/app10051635.
Hajjami, L.E., Mellouli, E. M., Žuraulis, V. and Berrada, M., 2022. Vehicle adaptive cruise controller based on an optimal super-twisting sliding mode control. 2nd International Conference of Smart Systems and Emerging Technologies (SMARTTECH), Riyadh, Saudi Arabia, 2022, pp. 160-165,
https://doi.org/10.1109/SMARTTECH54121.2022.00044.
Hassan, A. and Collier, G., 2014. Adaptive cruise control for a robotic vehicle using fuzzy logic. In: Březina, T., Jabloński, R. (eds) Mechatronics 2013. Springer, Cham. https://doi.org/10.1007/978-3-319-02294-9_68.
He, L., Yang, C., Wang, J., 2019. Simulation research on vehicle stability based on sliding mode variable structure control. 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT). Series: Advances in Computer Science Research. https://doi.org/10.2991/icmeit-19.2019.76.
He, Y., Ciuffo, B., Zhou, Q., Makridis, M., Mattas, K., Li, J., Li, Z., Yan, F. and Xu, H., 2019. Adaptive cruise control strategies implemented on experimental vehicles: A review. IFAC-PapersOnLine, 52(5), pp. 21-27. Available from: https://doi.org/10.1016/J.IFACOL.2019.09.004.
Idriz, A. F., 2015, Safe interaction between lateral and longitudinal adaptive cruise control in autonomous vehicles. M.Sc. Thesis at Delft University of Technology. http://resolver.tudelft.nl/uuid:adaa5dd4-10ca-42b3-b352-00bdc184657d
Islam, F., Nabi, M., Farhad, M., Peranich, P., Ball, J. and Goodin, C., 2021. Evaluating performance of extended Kalman filter based adaptive cruise control using PID controller. Proc. SPIE 11748, Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2021, P. 1174807; https://doi.org/10.1117/12.2585688.
Jahanshahi, N. and Ferrari, R.M.G., 2018. Attack detection and estimation in cooperative vehicles platoons: a sliding mode observer approach. IFAC-PapersOnLine, 51(23), pp. 212-217. https://doi.org/10.1016/j.ifacol.2018.12.037.
Jantzen, J., 1998. Design of fuzzy controllers. Technical report, Technical University of Denmark, Department of Automation.
Ji, X., Yang, K., Na, X., Lv, C., Liu, Y. and Yulong, L., 2019. Feedback game-based shared control scheme design for emergency collision avoidance: A fuzzy-LQR approach. Journal of Dynamic Systems Measurement and Control. 141. P. 081005. https://doi.org/10.1115/1.4042880.
Jia, Y., Jibrin, R., Itoh, Y. and Görges, D., 2019. Energy-optimal adaptive cruise control for electric vehicles in both time and space domain based on model predictive control, IFAC-PapersOnLine, 52(5), 2019, pp. 13-20. https://doi.org/10.1016/j.ifacol.2019.09.003.
Jiang, Y., Cai, L. and Jin, X., 2019. Optimization of adaptive cruise control system controller: using linear quadratic gaussian based on genetic algorithm. arXiv preprint arXiv:1911.08349.
https://doi.org/10.48550/arXiv.1911.08349.
Jiang, Y., 2020. Modeling and simulation of adaptive cruise control system. arXiv preprint arXiv:2008.02103. https://doi.org/10.48550/arXiv.2008.02103.
Khaled, A., Ricardo, M., Stephan, S., Daniel, G. and Raul, R., 2020. Fuzzy logic-based adaptive cruise control for autonomous model car. International Conference on Robotics, Computer Vision and Intelligent Systems. pp. 121-130. https://doi.org/10.5220/0010175101210130.
Kim, S.G., Tomizuka, M. and Cheng, K.H., 2012. Smooth motion control of the adaptive cruise control system by a virtual lead vehicle. Int. J Automot. Technol. 13, pp. 77–85. https://doi.org/10.1007/s12239-012-0007-6.
Ko, S. and Lee, J., 2007. Fuzzy logic based adaptive cruise control with guaranteed string stability. International Conference on Control, Automation and Systems, Seoul, Korea (South), pp. 15-20, https://doi.org/10.1109/ICCAS.2007.4406871.
Kudamble, B. G. and Jabeen, F., 2018. Improved intelligent adaptive cruise control for vehicle using fuzzy logic. International Journal of Engineering Research & Technology (IJERT), 4(21), pp. 1-4 https://doi.org/10.17577/IJERTCONV4IS21053.
Kural, E. and Güvenç, B.A., 2015. Integrated adaptive cruise control for parallel hybrid vehicle energy management, IFAC-PapersOnLine, 48(15), pp. 313-319,. https://doi.org/10.1016/j.ifacol.2015.10.045.
Lád, M., Herman, I. and Hurák, Z., 2017. Vehicular platooning experiments using autonomous slot cars. IFAC-PapersOnLine, 50(1), pp. 12596-12603. https://doi.org/10.1016/J.IFACOL.2017.08.2201.
Li, J., Liu, Y., Fotouhi, A., Wang, X., Zhang, Y., Li, L. and Chen, Z., 2023. Cooperative ecological adaptive cruise control for plug-in hybrid electric vehicle based on approximate dynamic programming. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/TVT.2022.3217354.
Li, S., Li, K., Rajamani, R. and Wang, J., 2011. Model predictive multi-objective vehicular adaptive cruise control. in IEEE Transactions on Control Systems Technology, 19(3), pp. 556-566. https://doi.org/10.1109/TCST.2010.2049203.
Li, S., Wang, H., Lu, X. and Yu, Z., 2023. Adaptive authority dynamic game for human-machine cooperative control. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. https://doi.org/10.1177/09544070231182458.
Li, X., Xie, N. and Wang, J., 2022. A variable weight adaptive cruise control strategy based on lane change recognition of leading vehicle, Automatika, 63(3), pp. 555-571, https://doi.org/10.1080/00051144.2022.2055913.
Li., Q., Gong, C. and Lin, Y., 2023. Co-optimisation of adaptive cruise control and hybrid electric vehicle energy management via model predictive mixed integer control. arXiv.org, https://doi.org/10.48550/arXiv.2303.01218.
Lu, C., Gong, J., Lv, C., Chen, X., Cao, D. and Chen, Y., 2019. A personalised behavior learning system for human-like longitudinal speed control of autonomous vehicles. Sensors 19, 17, P. 3672. https://doi.org/10.3390/s19173672.
Maciejowski, J.M., 2002. Predictive Control with Constraints. England.: Prentice Hall.
Mahadika, P., Subiantoro, A. and Kusumoputro, B., 2020. Neural network predictive control approach design for adaptive cruise control. International Journal of Technology. 11(7), pp. 1451-1462. https://doi.org/10.14716/ijtech.v11i7.4592.
Mehra, A., Ma, W., Berg, F., Tabuada, P., Grizzle, J.W. and Ames, A.D., 2015. Adaptive cruise control: Experimental validation of advanced controllers on scale-model cars. 2015 American Control Conference (ACC), Chicago, IL, USA, pp. 1411-1418, https://doi.org/10.1109/ACC.2015.7170931.
Memon, Z. A., Unar, M. and Pathan, D., 2012. Parametric study of nonlinear adaptive cruise control for a road vehicle model by MPC. Mehran University Research Journal of Engineering and Technology. P. 31. https://doi.org/10.1007/978-3-642-28962-0_9.
Mihaly, A. and Gáspár, P., 2013. Look-ahead cruise control considering road geometry and traffc flow. pp. 189-194. https://doi.org/10.1109/CINTI.2013.6705190.
Milanés, V. and Shladover, S,E., 2014. Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data. Transportation Research Part C-emerging Technologies, 48(48), pp. 285-300. https://doi.org/10.1016/J.TRC.2014.09.001.
Milanés, V., Villagrá, J., Godoy, J. and González, C., 2011. Comparing fuzzy and intelligent PI controllers in stop-and-go manoeuvres. IEEE Transactions on Control Systems Technology, 20(3), pp.770-778. https://doi.org/10.1109/TCST.2011.2135859
Mingyang, Z. and Gangfeng, T., 2023. Research on cooperative adaptive cruise control (CACC) based on fuzzy PID algorithm. SAE technical paper series, https://doi.org/10.4271/2023-01-0682.
Mo, H., Meng, Y., Wang, F.Y. and Wu, D., 2022. Interval type-2 fuzzy hierarchical adaptive cruise following-control for intelligent vehicles. IEEE/CAA Journal of Automatica Sinica, 9(9), pp. 1658-1672, https://doi.org/10.1109/JAS.2022.105806.
Mohammed, U., Hussein, S., Usman, M. and Thomas, S., 2020. Design of an optimal linear quadratic regulator (LQR) controller for the ball-on-sphere system. International Journal of Engineering and Manufacturing. 10, pp. 56-70. https://doi.org/10.5815/ijem.2020.03.05.
Naeem, H. M. Y. and Mahmood, A., 2016. Autonomous cruise control of car using LQR and H2 control algorithm. International Conference on Intelligent Systems Engineering (ICISE), Islamabad, Pakistan, pp. 123-128, https://doi.org/10.1109/INTELSE.2016.7475173.
Nie, Z., and Farzaneh, H., 2020. Adaptive cruise control for eco-driving based on model predictive control algorithm. Applied Sciences 10, 15, P. 5271. https://doi.org/10.3390/app10155271.
Nour, M., Chaves-Ávila, J.P., Magdy, G. and Sánchez-Miralles, Á., 2020. Review of positive and negative impacts of electric vehicles charging on electric power systems. Energies 13, 18, P. 4675. https://doi.org/10.3390/en13184675.
Ondoğan, A. and Yavuz, H. S., 2019. Fuzzy logic based adaptive cruise control for low-speed following. 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, Turkey, pp. 1-5, https://doi.org/10.1109/ISMSIT.2019.8932776.
Petri, A. and Petreuș, D.M., 2022. Adaptive cruise control in electric vehicles with field-oriented control. Applied Sciences 12, 14, P. 7094. https://doi.org/10.3390/app12147094.
Razzaghpour, M., Valiente, R., Zaman, M. and Fallah, Y.P., 2023. Control-aware communication for cooperative adaptive cruise control. arXiv: 2303.08076. https://doi.org/10.48550/arXiv.2303.08076.
Rizvi, R., Kalra, S., Gosalia, C. and Rahnamayan, S., 2014. Fuzzy adaptive cruise control system with speed sign detection capability," 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Beijing, China, pp. 968-976, https://doi.org/10.1109/FUZZ-IEEE.2014.6891748.
Rout, M. K., Sain, D., Swain, S. K. and Mishra, S. K., 2016. PID controller design for cruise control system using genetic algorithm. International Conference on Electrical, Electronics, and Optimisation Techniques (ICEEOT),
Chennai, India, pp. 4170-4174, https://doi.org/10.1109/ICEEOT.2016.7755502.
Samani, B. and Shamekhi, A. H., 2021. Real-time adaptive cruise controller with neural network model trained by multi objective model predictive controller data. Automotive Science and Engineering, 11(1),. https://doi.org/10.22068/ase.2021.580.
Samani, B., and Shamekhi, AH., 2022. Multi-objective adaptive cruise controller design using nonlinear predictive controller with the objective function with variable weights determined by fuzzy logic controller. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. 236(1), pp.142-154. https://doi.org/10.1177/09544070211014783.
Sathiyan, P., Cherian, M. and Pratap, B., 2020. Neural network based vehicle longitudinal controller -design and validation using hardware in loop testing. International Journal of Scientific & Technology Research. 9, pp. 7109-7114.
Shakouri, P. and Ordys, A., 2011. Application of the state-dependent nonlinear model predictive control in adaptive cruise control system. 14th International IEEE Conference on Intelligent Transportation Systems (ITSC),
Washington, DC, USA, pp. 686-691, https://doi.org/10.1109/ITSC.2011.6083030.
Shakouri, P., Ordys, A., Laila, D.S. and Askari, M., 2011.Adaptive cruise control system: comparing gain-scheduling PI and LQ controllers, IFAC Proceedings Volumes, 44(1), pp. 12964-12969. https://doi.org/10.3182/20110828-6-IT-1002.02250.
Sharma, A.K. and Bhushan, B., 2019. Sliding mode cruise control based on high gain observer. Journal of Emerging Technologies and Innovative Research (JETIR). 6(6), pp. 300-304 http://www.jetir.org/view?paper=JETIRCX06056.
Shladover, S.E., Nowakowski, C., Lu, X. and Ferlis, R., 2015. Cooperative Adaptive Cruise Control. Transportation Research Record, 2489, pp. 145-152. https://doi.org/10.3141/2489-17.
Singh, A., Satsangi, C. and Panse, P., 2015. Adaptive cruise control using fuzzy logic. International Journal of Digital Application & Contemporary Research. 3(8).
Sloiton, J. J. E. and Li, W., 1991. Applied nonlinear control. Englewood Cliffs: Prentice-Hall.
Sun, C., Chu L., Guo, J., Shi, D., Li, T. and Jiang, Y., 2017. Research on adaptive cruise control strategy of pure electric vehicle with braking energy recovery. Advances in Mechanical Engineering.9(11). https://doi.org/10.1177/1687814017734994.
Tagne, G., Talj, R. and Charara, A., 2013. Higher-order sliding mode control for lateral dynamics of autonomous vehicles, with experimental validation. In 2013 IEEE Intelligent Vehicles Symposium (IV) (pp. 678-683). IEEE.
Vajedi, M. and Azad, N., 2015. Ecological adaptive cruise controller for plug-in hybrid electric vehicles using nonlinear model predictive control. IEEE Transactions on Intelligent Transportation Systems. 17, pp.1-10. https://doi.org/10.1109/TITS.2015.2462843.
Wang, J., Zheng, H. and Zong, C., 2019. Longitudinal and lateral dynamics control of automatic lane change system. Transactions of the Institute of Measurement and Control. 41(15), pp. 4322-4338. https://doi.org/10.1177/0142331219856196.
Wang, L., 2009. Model predictive control system design and implementation using MATLAB (1st. ed.). Springer Publishing Company, Incorporated.
Wang, M., Yu, H., Dong, G. and Huang, M., 2019. Dual-mode adaptive cruise control strategy based on model predictive control and neural network for pure electric vehicles. 2019 5th International Conference on Transportation Information and Safety (ICTIS), Liverpool, UK, pp. 1220-1225, https://doi.org/10.1109/ICTIS.2019.8883435.
Wang, P., Wang, Y., Yu, G. Tang, T., 2014. An improved cooperative adaptive cruise control (CACC) algorithm considering invalid communication. Chin. J. Mech. Eng. 27, pp. 468–474. https://doi.org/10.3901/CJME.2014.03.468.
Wang, W., Cui, K., Gu, L. Lü, X., 2021. Cooperative Adaptive Cruise Control Using Delay-Based Spacing Policy: A Robust Adaptive Non-Singular Terminal Sliding Mode Approach. J. Shanghai Jiaotong Univ. (Sci.) 26, pp. 634–646. https://doi.org/10.1007/s12204-021-2353-x.
Wang, Y., Guo, Z., Wu, J. and Fu, S., 2022. Research on vehicle adaptive cruise control based on BP neural network working condition recognition. Journal of Engineering, 132, P. 147. https://doi.org/10.1049/tje2.12094.
Wu, X., Qin, G., Yu, H., Gao, S., Liu, L., Xue, Y., 2016. Using improved chaotic ant swarm to tune PID controller on cooperative adaptive cruise control. Optik, 127(6), pp. 3445-3450, https://doi.org/10.1016/j.ijleo.2015.12.014.
Xiang, W. and Shao, Z., 2022. Safety verification of neural network control systems using guaranteed neural network model reduction. IEEE 61st Conference on Decision and Control (CDC), Cancun, Mexico, pp. 1521-1526, https://doi.org/10.1109/CDC51059.2022.9992984.
Ya, Z., Wu, W. and Xiao-bo, J., 2013. Sliding mode control based on RBF neural networks. Fifth International Conference on Machine Vision (ICMV 2012): Computer Vision, Image Analysis and Processing. Proceedings of the SPIE, 8783, P. 87830I6. https://doi.org/10.1117/12.2013672.
Yang, M., and Jie T., 2023. Longitudinal and lateral stability control strategies for ACC systems of differential steering electric vehicles. Electronics, 12(19), P. 4178. https://doi.org/10.3390/electronics12194178.
Yao, J., Chen, G. and Gao, Z., 2021. Target vehicle selection algorithm for adaptive cruise control based on lane-changing intention of preceding vehicle. Chin. J. Mech. Eng. 34, P. 135. https://doi.org/10.1186/s10033-021-00650-8.
Yu, L. and Wang, R., 2022. Researches on adaptive cruise control system: A state of the art review. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 236(2-3), pp. 211-240. https://doi.org/10.1177/09544070211019254.
Zadeh, L.A., 1965. Fuzzy sets, Information and Control, 8(3), pp. 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X.
Zainuddin, M., Abdullah, M., Ahmad, S. and Tofrowaih, K.A., 2022. Performance comparison between predictive functional control and PID algorithms for automobile cruise control system. International Journal of Automotive and Mechanical Engineering, 19(1), pp. 9460-68. https://doi.org/10.15282/ijame.19.1.2022.09.0728.
Zhang, D., Li, K. and Wang, J., 2012. A curving ACC system with coordination control of longitudinal car-following and lateral stability, Vehicle System Dynamics, 50(7), pp. 1085-1102, https://doi.org/10.1080/00423114.2012.656654.
Zhu, Z., Bei, S., Li, B., Liu, G., Tang, H., Zhu, Y., and Gao,C., 2023. Research on robust control of intelligent vehicle adaptive cruise. World Electric Vehicle Journal. 14(10), P. 268. https://doi.org/10.3390/ wevj14100268.