Autonomous driving research currently faces data sparsity in representation of risky scenarios. Such data is both difficult to obtain ethically in the real world, and unreliable to obtain via simulation. Recent advances in virtual reality (VR) driving simulators lower barriers to tackling this problem in simulation. We propose the first data collection framework for risky scenario driving data from real humans using VR, as well as accompanying numerical driving personality characterizations. We validate the resulting dataset with statistical analyses and model driving behavior with an eight-factor personality vector based on the Multi-dimensional Driving Style Inventory (MDSI). Our method, dataset, and analyses show that realistic driving personalities can be modeled without deep learning or large datasets to complement autonomous driving research.

翻译：自动驾驶研究目前面临风险场景数据稀疏的挑战。此类数据既难以在真实世界中以合乎伦理的方式获取，也难以通过仿真可靠地获得。近年虚拟现实驾驶模拟器的进展降低了在仿真中解决这一问题的门槛。我们提出了首个利用虚拟现实从真实人类驾驶者采集风险场景驾驶数据的框架，以及配套的数值化驾驶个性特征描述方法。通过统计分析验证所得数据集的有效性，并基于多维驾驶风格量表构建八因子个性向量对驾驶行为进行建模。我们的方法、数据集及分析表明，无需深度学习或大规模数据集即可建模具有真实感的驾驶个性，从而助力自动驾驶研究。