Based on the artificial potential field theory, a hierarchical obstacle avoidance assisted driving framework is proposed in this paper, in order to solve the obstacle avoidance assisted driving problem of distributed heavy vehicles. Initially, a desired lateral position potential field (DLPPF) that reflects the driver’s driving intention is applied based on the constant turn rate and velocity (CTRV) model. And a real-time obstacle avoidance path can be obtained and further updated through a comprehensive consideration of the safety of obstacle avoidance and the obstacle repulsion potential field (ORPF). In addition, a differential drive assistance steering (DDAS) on the heavy vehicle steering bridge has been adopted to realize avoiding obstacles, and additional yaw moment to the vehicle’s non-steering axle has been utilized to ensure the maneuverability and stability of vehicle. Aiming to solve the lower layer control problem, a variable weight controller based on model predictive control (MPC) is designed to coordinate path tracking performance and dynamics control. Finally, through simulations and comparisons, the effectiveness of the obstacle avoidance strategy is verified.
MarcanoMDiazSPerezJ, et al. A review of shared control for automated vehicles: theory and applications. IEEE Trans Hum Mach Syst2020; 50(6): 475–491.
2.
WangWNaXCaoD, et al. Decision-making in driver-automation shared control: a review and perspectives. IEEE/CAA J Automat Sin2020; 7: 1289–1307.
3.
SO-RAVS Committee. Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. SAE Standard J3016, 2016: 1–16.
4.
LeeHRaMKimWY.Nighttime data augmentation using GAN for improving blind-spot detection. IEEE Access2020; 8: 48049–48059.
5.
AnnellSGratnerASvenssonL. Probabilistic collision estimation system for autonomous vehicles. In: IEEE 19th international conference on intelligent transportation systems (ITSC), 2016.
6.
SaitoYItohMInagakiT.Driver assistance system with a dual control scheme: effectiveness of identifying driver drowsiness and preventing lane departure accidents. IEEE Trans Hum Mach Syst2016; 46(5): 660–671.
7.
TadaSSonodaKWadaT.Simultaneous achievement of workload reduction and skill enhancement in backward parking by haptic guidance. IEEE Trans Intell Vehicles2017; 1(4): 292–301.
8.
WangWXiJLiuC, et al. Human-centered feed-forward control of a vehicle steering system based on a driver’s path-following characteristics. IEEE Trans Intell Transp Syst2017; 18(6): 1440–1453.
9.
JiangJAstolfiA.Shared-control for a rear-wheel drive car: dynamic environments and disturbance rejection. IEEE Trans Hum Mach Syst2017; 47(5): 723–734.
10.
NguyenA-TSentouhCPopieulJ-C.Driver-automation cooperative approach for shared steering control under multiple system constraints: design and experiments. IEEE Trans Ind Electron2017; 64(5): 3819–3830.
11.
OufroukhNAMammarS. Integrated driver co-pilote approach for vehicle lateral control. In: Intelligent vehicles symposium proceedings, 2014, pp.1163–1168. New York, NY: IEEE.
12.
LiMSongXCaoH, et al. Shared control with a novel dynamic authority allocation strategy based on game theory and driving safety field. Mech Syst Signal Process2019; 124: 199–216.
13.
JiXYangKNaX, et al. Shared steering torque control for lane change assistance: a stochastic game-theoretic approach. IEEE Trans Ind Electron2019; 66(4): 3093–3105.
14.
IsermannRSchornMStählinU.Anticollision system PRORETA with automatic braking and steering. Veh Syst Dyn2008; 46: 683–694.
15.
BenloucifANguyenATSentouhC, et al. Cooperative trajectory planning for haptic shared control between driver and automation in highway driving. IEEE Trans Ind Electron2019; 66(12): 9846–9857.
16.
RasekhipourYKhajepourAChenSK, et al. A potential field-based model predictive path-planning controller for autonomous road vehicles. IEEE Trans Intell Transp Syst2017; 18(5): 1255–1267.
17.
JiJKhajepourAMelekWW, et al. Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints. IEEE Trans Vehicular Technol2017; 66: 952–964.
18.
HuangYDingHZhangY, et al. A motion planning and tracking framework for autonomous vehicles based on artificial potential field elaborated resistance network approach. IEEE Trans Ind Electron2020; 67: 1376–1386.
19.
BrandtTSattelTBohmM.Combining haptic human-machine interaction with predictive path planning for lane-keeping and collision avoidance systems. In: Proceedings of IEEE intelligent vehicles symposium, June2007, pp.582–587. New York, NY: IEEE.
20.
LiMCaoHSongX, et al. Shared control driver assistance system based on driving intention and situation assessment. IEEE Trans Ind Inform2018; 14(11): 4982–4994.
21.
ChenKYamaguchiTOkudaH, et al. Realization and evaluation of an instructor-like assistance system for collision avoidance. IEEE Trans Intell Transp Syst2021; 22(5): 2751–2760.
22.
GuoNZhangXZouY, et al. A supervisory control strategy of distributed drive electric vehicles for coordinating handling, lateral stability, and energy efficiency. IEEE Trans Transp Electrification2021; 7: 2488–2504.
23.
SalehLChevrelPClaveauF, et al. Shared steering control between a driver and an automation: Stability in the presence of driver behavior uncertainty. IEEE Trans Intell Transp Syst2013; 14(2): 974–983.
24.
ErcanZCarvalhoATsengHE, et al. A predictive control framework for torque-based steering assistance to improve safety in highway driving. Veh Syst Dyn2018; 56(5): 810–831.
25.
WangJWangQJinL, et al. Independent wheel torque control of 4WD electric vehicle for differential drive assisted steering. Mechatronics2011; 21: 63–76.
26.
HuCWangRYanF, et al. Robust composite nonlinear feedback path-following control for independently actuated autonomous vehicles with differential steering. IEEE Trans Transp Electrification2016; 2(3): 312–321.
27.
WangRJingHHuC, et al. Robust H∞ output-feedback yaw control for in-wheel motor driven electric vehicles with differential steering. Neurocomputing2016; 173: 676–684.
28.
SchubertRRichterEWanielikG.Comparison and evaluation of advanced motion models for vehicle tracking. In:IEEE 11th international conference on information fusion, 2008, pp.730–735. New York, NY: IEEE.
29.
LuBHeHYuH, et al. Hybrid path planning combining potential field with sigmoid curve for autonomous driving. Sensors2020; 20(24): 7197.
30.
ZouYGuoNZhangX.An integrated control strategy of path following and lateral motion stabilization for autonomous distributed drive electric vehicles. Proc IMechE, Part D: J Automobile Engineering2019; 235(4): 1164–1179.
31.
GuoNLenzoBZhangX, et al. A real-time nonlinear model predictive controller for yaw motion optimization of distributed drive electric vehicles. IEEE Trans Vehicular Technol2020; 69(5): 4935–4946.
