期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 67, 期 6, 页码 4771-4782出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2018.2804891
关键词
Automated driving control; lane change decision; real-time implementation; stochastic model predictive control; vehicle test
资金
- Institute of Advanced Machines and Design, Seoul National University
- Brain Korea 21 program [F14SN02D1310]
- Korea Ministry of Land, Infrastructure and Transport
- Korea Agency for Infrastructure Technology Advancement [16PTSI-C054118-08]
This paper describes lane change motion planning with a combination of probabilistic and deterministic prediction for automated driving under complex driving circumstances. The autonomous lane change should arrive safely at the destination. The subject vehicle needs to perceive and predict the behaviors of other vehicles with sensors. From the information of other vehicles, a collision probability is defined using a reachable set of uncertainty propagation. In addition, the lane change risk is monitored using predicted time-to-collision and safety distance to guarantee safety in lane change behavior. A safe driving envelope is defined as constraints based on the combinatorial prediction (probabilistic and deterministic) of the behavior of surrounding vehicles. To obtain the desired steering angle and longitudinal acceleration to maintain the automated driving vehicle under constraints, a stochastic model-predictive control problem is formulated. The proposed model has been evaluated by performing lane change simulations in MATLAB/Simulink, while considering the effect of combination prediction. Also, the proposed algorithm has been implemented on a test vehicle. The simulation and test results show that the proposed algorithm can handle complicated lane change scenarios, while guaranteeing safety.
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