论文标题
通过人类内部状态通过POMDP计划改善自动驾驶
Improving Automated Driving through POMDP Planning with Human Internal States
论文作者
论文摘要
这项工作研究了以下假设：与人驾驶状态的部分可观察到的马尔可夫决策过程（POMDP）计划可以显着提高自动高速公路驾驶的安全性和效率。我们在模拟场景中评估了这一假设，即自动驾驶汽车必须在快速连续中安全地进行三个车道变化。通过观测扩大（POMCPOW）算法，通过部分可观察到的蒙特卡洛计划获得了近似POMDP溶液。这种方法的表现优于过于自信和保守的MDP基线，匹配或匹配效果优于QMDP。相对于MDP基准，POMCPOW通常将不安全情况的速率降低了一半，或者将成功率提高了50％。
This work examines the hypothesis that partially observable Markov decision process (POMDP) planning with human driver internal states can significantly improve both safety and efficiency in autonomous freeway driving. We evaluate this hypothesis in a simulated scenario where an autonomous car must safely perform three lane changes in rapid succession. Approximate POMDP solutions are obtained through the partially observable Monte Carlo planning with observation widening (POMCPOW) algorithm. This approach outperforms over-confident and conservative MDP baselines and matches or outperforms QMDP. Relative to the MDP baselines, POMCPOW typically cuts the rate of unsafe situations in half or increases the success rate by 50%.
