论文标题
风格预示：轨迹的人类驾驶员行为的机器心理理论
StylePredict: Machine Theory of Mind for Human Driver Behavior From Trajectories
论文作者
论文摘要
研究表明，自动驾驶汽车（AVS）在由人类驾驶员组成的交通环境中保守地行为，并且不适合当地条件和社会文化规范。众所周知，如果存在一种理解人类驱动因素行为的机制，则可以设计具有社会意识的AV。我们提出了机器心理理论（M-TOM）的概念，以通过观察其车辆的轨迹来推断人类驾驶员的行为。我们的M-TOM方法称为StylePredict，基于对车辆的轨迹分析，该方法已在机器人技术和计算机视觉中进行了研究。 Style predict模仿了人类TOM，以使用图形理论技术（包括光谱分析和中心函数）在交通中提取的车辆的提取轨迹与驾驶员行为之间的计算映射来推断驾驶员行为或样式。我们根据交通密度，异质性和符合交通规则的合规性，使用Style Predict来分析美国，中国，印度和新加坡不同文化中的驾驶员行为，并观察到纵向（超速）和横向（超越，车道，车道变换）驾驶样式之间的相关性。
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 exist a mechanism to understand the behaviors of human drivers. We present a notion of Machine Theory of Mind (M-ToM) to infer the behaviors of human drivers by observing the trajectory of their vehicles. Our M-ToM approach, called StylePredict, is based on trajectory analysis of vehicles, which has been investigated in robotics and computer vision. StylePredict mimics human ToM to infer driver behaviors, or styles, using a computational mapping between the extracted trajectory of a vehicle in traffic and the driver behaviors using graph-theoretic techniques, including spectral analysis and centrality functions. We use StylePredict to analyze driver behavior in different cultures in the USA, China, India, and Singapore, based on traffic density, heterogeneity, and conformity to traffic rules and observe an inverse correlation between longitudinal (overspeeding) and lateral (overtaking, lane-changes) driving styles.
