Studies have shown that autonomous vehicles (AVs) behave conservatively in a traffic environment composed of human drivers and do not adapt to local conditions and socio-cultural norms. It is known that socially aware AVs can be designed if there exists a mechanism to understand the behaviors of human drivers. We present an approach that leverages machine learning to predict, the behaviors of human drivers. This is similar to how humans implicitly interpret the behaviors of drivers on the road, by only observing the trajectories of their vehicles. We use graph-theoretic tools to extract driver behavior features from the trajectories and machine learning to obtain a computational mapping between the extracted trajectory of a vehicle in traffic and the driver behaviors. Compared to prior approaches in this domain, we prove that our method is robust, general, and extendable to broad-ranging applications such as autonomous navigation. We evaluate our approach on real-world traffic datasets captured in the U.S., India, China, and Singapore, as well as in simulation.
翻译:研究表明,自主车辆(AVs)在由人驾驶员组成的交通环境中行事保守,不适应当地条件和社会文化规范。众所周知,如果存在了解人驾驶员行为的机制,社会意识的AVs是可以设计出来的。我们展示了一种利用机器学习来预测人驾驶员行为的方法。这与人如何通过观察其车辆的轨迹来暗中解释驾驶员在路上的行为相似。我们使用图形理论工具从轨迹和机器学习中提取驾驶员行为特征,以获得车辆交通和驾驶员行为轨迹的计算图。与以前在这一领域的做法相比,我们证明我们的方法是稳健的、一般的,可以推广到诸如自主导航等广泛应用。我们评估了我们在美国、印度、中国和新加坡所捕捉的实时交通数据集以及模拟中的方法。