The abilities to understand the social interaction behaviors between a vehicle and its surroundings while predicting its trajectory in an urban environment are critical for road safety in autonomous driving. Social interactions are hard to explain because of their uncertainty. In recent years, neural network-based methods have been widely used for trajectory prediction and have been shown to outperform hand-crafted methods. However, these methods suffer from their lack of interpretability. In order to overcome this limitation, we combine the interpretability of a discrete choice model with the high accuracy of a neural network-based model for the task of vehicle trajectory prediction in an interactive environment. We implement and evaluate our model using the INTERACTION dataset and demonstrate the effectiveness of our proposed architecture to explain its predictions without compromising the accuracy.

翻译：理解车辆与其周围环境之间的社会交互行为，并在城市环境中预测其轨迹的能力，对于自动驾驶的道路安全至关重要。由于社会交互具有不确定性，其解释难度较大。近年来，基于神经网络的方法已广泛应用于轨迹预测，并被证明优于手工建模方法。然而，这类方法存在可解释性不足的缺陷。为克服这一局限，我们将离散选择模型的可解释性与基于神经网络模型的高精度相结合，用于交互环境中的车辆轨迹预测任务。我们采用INTERACTION数据集实现并评估了所提模型，结果表明，所提出的架构在保持预测精度的同时，能够有效解释其预测结果。