期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 22, 期 5, 页码 3135-3145出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2020.2982002
关键词
Safety; Vehicles; Decision making; Trajectory; Computer architecture; Process control; Wheels; Human-like steering control; precognitive architecture; universal scheme; safety zone; driving simulator
资金
- National Natural Science Foundation of China [51805290]
This paper proposes a novel control scheme for automated steering systems based on human control behavior patterns, providing a universal solution for steering control in various driving scenarios. Simulation experiments demonstrate that the novel scheme outperforms traditional control schemes in steering performance, steering effort, and robustness.
To incorporate the inherent superiorities of an experienced, proficient driver, a human-like control scheme for automated steering systems is proposed in this paper based on the concept of staying within the safety zone. Designed in the precognitive architecture, the novel control scheme takes into account key characteristics of human steering behaviors in high-speed conditions by determining the steering activation and the steering intensity based on the inversed-time-to-lane-crossing (iTLC), generating smooth, moderate steering actions to maintain the vehicle within a predefined safety zone. The proposed control scheme provides a universal solution for steering control in multiple driving scenarios by accordingly allocating the safety zone to ensure the driving safety therein. A simulator experiment was conducted to test both the fully autonomous and the driver-in-the-loop performance of the proposed control scheme in lane keeping and lane changing. A traditional error-minimizing control algorithm was implemented as a comparison. Results indicate that the novel scheme outperforms the conventional control schemes by yielding higher steering performance, lower steering effort, and higher robustness, proving the benefits of imitating the patterns of human control behaviors in the control algorithm of an automated steering system.
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