Adaptive Cruise Control with Look-Ahead Anticipation for Driving on Freeways
Source abstract
Abstract
This paper presents Adaptive Cruise Control (ACC) with look-ahead anticipation, based on the model of ACC used in recent commercial vehicles, to take early decisions in driving a vehicle on the freeway. The existing ACC found in the high-end cars has limited operating range as it often fails to respond smoothly in advance behind a decelerating vehicle. Although advanced techniques, such as model predictive control (MPC), can significantly improve a vehicle’s driving performance, they are associated with high computational complexity and have limited scopes for practical implementation. The proposed look-ahead anticipatory scheme of ACC predicts the relative states of the preceding vehicle using a conditional persistence prediction technique in an adaptive short horizon. With negligible computation cost, it determines the control input using parametric functions prudently for improving driving performance. The proposed scheme is evaluated on multiple vehicles in typical traffic scenarios to examine individual driving behavior and the stability of a vehicle string. Finally, we investigate the influences of a small part of vehicles with the proposed ACC on overall traffic using the AIMSUN traffic simulator and compare performances of overall traffic.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Traffic control and management · Traffic Prediction and Management Techniques · Autonomous Vehicle Technology and Safety
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Graduate School of Science and Technology, Gunma University, Kiryu 376-8515, Japan
School of Engineering, Tokyo Institute of Technology, Tokyo 152-8552, Japan
Funding
Japan Society for the Promotion of Science
References (41)
Show source-provided references
1. A behavioural car-following model for computer simulation10.1016/0191-2615(81)90037-0 ↗
2. Stabilization effect of traffic flow in an extended car-following model based on an intelligent transportation system application10.1103/PhysRevE.70.066134 ↗
3. Stability analysis of an extended intelligent driver model and its simulations under open boundary condition10.1016/j.physa.2014.10.063 ↗
4. Robust local and string stability for a decentralized car following control strategy for connected automated vehicles10.1016/j.trb.2019.05.003 ↗
5. Towards an understanding of adaptive cruise control10.1016/S0968-090X(00)00022-X ↗
6. Research advances in intelligent collision avoidance and adaptive cruise control10.1109/TITS.2003.821292 ↗
7. ACC+ Stop&go maneuvers with throttle and brake fuzzy control10.1109/TITS.2006.874723 ↗
8. Design, tuning, and evaluation of a full-range adaptive cruise control system with collision avoidance10.1016/j.conengprac.2008.09.006 ↗
9. Adaptive cruise control design for active congestion avoidance10.1016/j.trc.2007.12.004 ↗
10. Model predictive multi-objective vehicular adaptive cruise control10.1109/TCST.2010.2049203 ↗
11. Real time adaptive cruise control strategy for motorways10.1016/j.trc.2020.102617 ↗
12. Effect of adaptive cruise control systems on traffic flow10.1103/PhysRevE.69.066110 ↗
13. Cooperative adaptive cruise control in real traffic situations
14. Cooperative adaptive cruise control for connected autonomous vehicles by factoring communication-related constraints10.1016/j.trc.2019.04.010 ↗
15. The impact of cooperative adaptive cruise control on traffic-flow characteristics10.1109/TITS.2006.884615 ↗
16. Tsugawa, S., Kato, S., and Aoki, K. (2011, January 25–30). An automated truck platoon for energy saving. Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, USA.10.1109/IROS.2011.6094549 ↗
17. A review of truck platooning projects for energy savings10.1109/TIV.2016.2577499 ↗
18. Heavy-duty vehicle platoon formation for fuel efficiency10.1109/TITS.2015.2492243 ↗
19. An Ecological Adaptive Cruise Control for Mixed Traffic and Its Stabilization Effect10.1109/ACCESS.2019.2923741 ↗
20. Stability analysis of connected and automated vehicles to reduce fuel consumption and emissions10.1061/JTEPBS.0000196 ↗
21. Influence of connected and autonomous vehicles on traffic flow stability and throughput10.1016/j.trc.2016.07.007 ↗
22. Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data10.1016/j.trc.2014.09.001 ↗
23. Unravelling effects of cooperative adaptive cruise control deactivation on traffic flow characteristics at merging bottlenecks10.1016/j.trc.2018.10.008 ↗
24. Evaluation of ACC vehicles in mixed traffic: Lane change effects and sensitivity analysis10.1109/TITS.2005.844226 ↗
25. Fine-tuning ADAS algorithm parameters for optimizing traffic safety and mobility in connected vehicle environment10.1016/j.trc.2017.01.003 ↗
26. Optimal ecodriving control: Energy-efficient driving of road vehicles as an optimal control problem10.1109/MCS.2015.2449688 ↗
27. Nie, Z., and Farzaneh, H. (2020). Adaptive Cruise Control for Eco-Driving Based on Model Predictive Control Algorithm. Appl. Sci., 10.10.3390/app10155271 ↗
28. Fast model predictive control-based fuel efficient control strategy for a group of connected vehicles in urban road conditions10.1109/TCST.2016.2572603 ↗
29. Intersection management via vehicle connectivity: The intersection cooperative adaptive cruise control system concept10.1080/15472450.2014.889918 ↗
30. Road-speed profile for enhanced perception of traffic conditions in a partially connected vehicle environment10.1109/TVT.2018.2826067 ↗
31. Efficient driving on multilane roads under a connected vehicle environment10.1109/TITS.2016.2519526 ↗
32. Are Commercially Implemented Adaptive Cruise Control Systems String Stable?10.1109/TITS.2020.3000682 ↗
33. Congested traffic states in empirical observations and microscopic simulations10.1103/PhysRevE.62.1805 ↗
34. Kamal, M.A.S., Hashikura, K., Hayakawa, T., Ogitsu, T., Yamada, K., and Imura, J. (2020, January 12–17). A Look-ahead Car Following Scheme for Efficient Driving on Urban Roads. Proceedings of the 2020 IFAC World Congress, Berlin, Germany.10.1016/j.ifacol.2020.12.894 ↗
35. Kamal, M.A.S., Hashikura, K., Hayakawa, T., Yamada, K., and Imura, J.i. (2021). Look-ahead Driving Schemes for Efficient Control of Automated Vehicles on Urban Roads. IEEE Trans. Veh. Technol.10.1109/TVT.2021.3132936 ↗
36. Ecological vehicle control on roads with up-down slopes10.1109/TITS.2011.2112648 ↗
37. Akçelik, R. (1983). Progress in Fuel Consumption Modelling for Urban Traffic Management, Australian Road Research Board.
38. Schakel, W.J., Van Arem, B., and Netten, B.D. (2010, January 19–22). Effects of cooperative adaptive cruise control on traffic flow stability. Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems, Funchal, Portugal.10.1109/ITSC.2010.5625133 ↗
39. Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels10.1061/(ASCE)0733-947X(2002)128:2(182) ↗
40. Barceló, J., and Casas, J. (2005). Dynamic network simulation with AIMSUN. Simulation Approaches in Transportation Analysis, Springer.10.1007/0-387-24109-4_3 ↗
41. Smart driving of a vehicle using model predictive control for improving traffic flow10.1109/TITS.2013.2292500 ↗