期刊
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING
卷 235, 期 14, 页码 3322-3338出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/09544070211010598
关键词
Characteristic identification; artificial potential field; LSTM; self-learning; lane change planning
资金
- National Natural Science Foundation of China [51775236]
- National Key R&D Program of China [2017YFB0102600]
This paper proposes a self-learning lane change motion planning system considering the driver's personality, and explains the driver data acquisition and processing methods, as well as the planning system. It evaluates the acceptance of environmental risks of different drivers by establishing an obstacle driving risk field, and achieves personalized lane change triggers through online statistics.
Nowadays, with more and more attention being paid to the characteristics and experience of drivers, a large number of driver classification algorithms have emerged. However, these methods basically cannot be adjusted independently to each driver. Therefore, this paper proposes a self-learning lane change motion planning system considering the driver's personality. Firstly, the method of driver data acquisition and processing is determined to obtain and extract the lane change data. Then, the planning system built in this paper is explained from two aspects: lane change trigger and lane change trajectory. According to the artificial potential field theory, an obstacle driving risk field is established to evaluate the acceptance of environmental risks of different drivers, and to achieve personalized lane change triggers through online statistics. At the same time, the safety of lane change is ensured by establishing the safety distance model of the target lane. On the other hand, the driver characteristic coefficient J(c) and the driver reaction and operation time t(d) are introduced into the traditional Gaussian-distributed model to establish a personalized lane change trajectory planning model, in which the parameters are obtained from offline and online learning. Offline learning is based on DTW for trajectory matching, and uses AP clustering to obtain the generalized parameters; Online learning uses LSTM to achieve personalized updates. Finally, this paper selected 15 drivers for verification, and the results show that the motion planning system can well reproduce the lane change behavior of the driver.
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