Localisation and navigation of autonomous vehicles (AVs) in static environments are now solved problems, but how to control their interactions with other road users in mixed traffic environments, especially with pedestrians, remains an open question. Recent work has begun to apply game theory to model and control AV-pedestrian interactions as they compete for space on the road whilst trying to avoid collisions. But this game theory model has been developed only in unrealistic lab environments. To improve their realism, this study empirically examines pedestrian behaviour during road crossing in the presence of approaching autonomous vehicles in more realistic virtual reality (VR) environments. The autonomous vehicles are controlled using game theory, and this study seeks to find the best parameters for these controls to produce comfortable interactions for the pedestrians. In a first experiment, participants’ trajectories reveal a more cautious crossing behaviour in VR than in previous laboratory experiments. In two further experiments, a gradient descent approach is used to investigate participants’ preference for AV driving style. The results show that the majority of participants were not expecting the AV to stop in some scenarios, and there was no change in their crossing behaviour in two environments and with different car models suggestive of car and last-mile style vehicles. These results provide some initial estimates for game theoretic parameters needed by future AVs in their pedestrian interactions and more generally show how such parameters can be inferred from virtual reality experiments.

现在已经解决了静态环境中自动驾驶汽车（AV）的本地化和导航问题，但是如何在混合交通环境中（尤其是与行人）控制其与其他道路使用者的交互仍然是一个悬而未决的问题。最近的工作已开始将博弈论应用于模拟和控制AV人行道交互，因为它们在争夺道路空间的同时试图避免碰撞。但是，这种博弈论模型仅在不切实际的实验室环境中开发。为了提高他们的真实感，本研究以实在的方式研究了在更逼真的虚拟现实（VR）环境中，在接近自动驾驶汽车的情况下，过马路时行人的行为。自动驾驶汽车采用博弈论进行控制，本研究旨在为这些控件找到最佳参数，以使行人产生舒适的互动。在第一个实验中，参与者的轨迹显示，与以前的实验室实验相比，VR中的穿越行为更为谨慎。在另外两个实验中，使用梯度下降法研究参与者对AV驾驶风格的偏好。结果表明，大多数参与者并不期望AV在某些情况下会停止，并且在两种环境中以及具有暗示汽车和最后一英里风格的汽车的不同汽车模型的情况下，他们的穿越行为没有变化。