For driving safely and efficiently in highway scenarios, autonomous vehicles (AVs) must be able to predict future behaviors of surrounding object vehicles (OVs), and assess collision risk accurately for reasonable decision-making. Aiming at autonomous driving in highway scenarios, a predictive collision risk assessment method based on trajectory prediction of OVs is proposed in this paper. Firstly, the vehicle trajectory prediction is formulated as a sequence generation task with long short-term memory (LSTM) encoder-decoder framework. Convolutional social pooling (CSP) and graph attention network (GAN) are adopted for extracting local spatial vehicle interactions and distant spatial vehicle interactions, respectively. Then, two basic risk metrics, time-to-collision (TTC) and minimal distance margin (MDM), are calculated between the predicted trajectory of OV and the candidate trajectory of AV. Consequently, a time-continuous risk function is constructed with temporal and spatial risk metrics. Finally, the vehicle trajectory prediction model CSP-GAN-LSTM is evaluated on two public highway datasets. The quantitative results indicate that the proposed CSP-GAN-LSTM model outperforms the existing state-of-the-art (SOTA) methods in terms of position prediction accuracy. Besides, simulation results in typical highway scenarios further validate the feasibility and effectiveness of the proposed predictive collision risk assessment method.

翻译：为实现高速公路场景下的安全高效驾驶，自动驾驶车辆需具备预测周围目标车辆未来行为的能力，并准确评估碰撞风险以做出合理决策。针对高速公路自动驾驶场景，本文提出一种基于目标车辆轨迹预测的预测性碰撞风险评估方法。首先，采用长短期记忆编码器-解码器框架将车辆轨迹预测建模为序列生成任务。分别采用卷积社交池化和图注意力网络提取局部空间车辆交互和远距离空间车辆交互。然后，在目标车辆预测轨迹与自动驾驶车辆候选轨迹之间计算两个基础风险指标：碰撞时间（TTC）和最小距离裕度（MDM）。据此，通过时空风险指标构建时间连续风险函数。最后，在两个公开高速公路数据集上评估轨迹预测模型CSP-GAN-LSTM。定量结果表明，所提出的CSP-GAN-LSTM模型在位置预测精度上优于现有最先进方法。此外，典型高速公路场景的仿真结果进一步验证了所提出的预测性碰撞风险评估方法的可行性和有效性。