Name
Abstract | ||
|---|---|---|
Lane change is a crucial vehicle maneuver which needs coordination with surrounding vehicles. Automated lane changing functions built on rule-based models may perform well under pre-defined operating conditions, but they may be prone to failure when unexpected situations are encountered. In our study, we proposed a Reinforcement Learning based approach to train the vehicle agent to learn an automated lane change behavior such that it can intelligently make a lane change under diverse and even unforeseen scenarios. Particularly, we treated both state space and action space as continuous, and designed a Q-function approximator that has a closed-form greedy policy, which contributes to the computation efficiency of our deep Q-learning algorithm. Extensive simulations are conducted for training the algorithm, and the results illustrate that the Reinforcement Learning based vehicle agent is capable of learning a smooth and efficient driving policy for lane change maneuvers. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ivs.2018.8500556 | Intelligent Vehicles Symposium |
DocType | Volume | Citations |
Conference | abs/1804.07871 | 2 |
PageRank | References | Authors |
0.45 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wang, Pin | 1 | 6 | 3.06 |
| Ching-Yao Chan | 2 | 79 | 23.48 |
| Arnaud de La Fortelle | 3 | 264 | 31.52 |