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Lv, Chen; Mouzakitis, Alexandros; Wang, Huaji; Cao, Dongpu; Zhao, Yifan; Sullman, Mark; Auger, Daniel J.; Brighton, James; Matthias, Rebecca; Skrypchuk, Lee
2018 IEEE Intelligent Vehicles Symposium (IV), 2018-JuneConference Proceeding
Autonomous driving presents an exciting new development in vehicle technology. It poses a new challenge in driver-automation collaboration particularly during handover transitions between human and machine. In order to deal with this problem, this paper proposes a novel control framework for the haptic take-over system. The high-level framework of the haptic take-over control system, which takes driver cognitive workload, neuromuscular dynamics and optimal trajectory planning into consideration, is developed. Under the proposed framework, the determination approach of the optimal input sequence is introduced. The model of the allowed driver take-over authority, which is associated with driver's cognitive workload, as well as muscle readiness during take- over, is investigated and developed. The haptic feedback torque controller is then designed so as to minimize the deviation between the allowed control authority and driver's current degree of participation. A handover process, along with the proposed take-over control method, is also simulated. The simulation results validate the feasibility and effectiveness of the proposed approach.
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