摘要：

轨迹预测和碰撞检测是自动驾驶的关键技术，可以提高自动驾驶系统对周围环境的感知能力，保障自动驾驶系统的安全性。Conv-LSTM模型能够有效处理具有时空相关性的轨迹数据，具有良好的轨迹预测能力，但该模型在交通拥堵、复杂道路等复杂情形下预测性能较差。提出一种基于行驶意图识别的轨迹预测模型。通过基于长短期记忆（LSTM）网络的行驶意图识别模块对车辆的行驶意图进行预测，基于Conv-LSTM构建轨迹预测模块，结合识别的行驶意图信息预测未来轨迹，从而提高轨迹预测的精度和可解释性。引入2种注意力机制对目标对象及其周围车辆的历史轨迹信息进行重要性分析，使模型关注最具有代表性的邻居车辆，并且更好地捕捉不同时间步之间的关系，从而提高模型的预测准确度和稳定性。针对有向包围盒碰撞检测算法执行效率低的问题，提出一种基于混合包围盒的碰撞检测算法，通过最小安全距离和最大冲突距离进行碰撞预判断，避免非冲突情况下有向包围盒的创建和基于分离轴定理的碰撞检测过程，从而提高碰撞检测的效率。在NGSIM数据集上进行实验，结果表明：该模型的均方根误差优于Conv-LSTM、sys-Conv等对比模型，轨迹预测的精度更高；与有向包围盒(OBB)算法、轴对齐包围盒(AABB)算法和AABB-OBB算法相比，基于混合包围盒的碰撞检测算法平均碰撞检测时间分别缩短了64.47%、53.88%和55.47%。

关键词: 轨迹预测, 碰撞检测, 自动驾驶, 注意力机制, 意图识别, 混合包围盒

Abstract:

In autonomous driving, trajectory prediction and collision detection are the key technologies that can improve the perception ability of the autonomous driving system for the surrounding environment and ensure driving safety. The Conv-LSTM model displays good trajectory prediction ability, effectively processing trajectory data with spatio-temporal correlation. However, the predictive ability of this model is relatively weak in complex situations, such as traffic congestion and complex roads. Therefore, this study proposes a trajectory prediction model for driving intention identification based on Long Short-Term Memory(LSTM) network.The trajectory prediction model is constructed based on Conv-LSTM and uses the identified driving intention information to predict future trajectories, improving the accuracy and interpretability of trajectory prediction. In addition, two attention mechanisms are introduced to analyze the importance of the historical trajectory information of the target object and surrounding vehicles, which enables the model to focus on the most representative neighboring vehicles to better capture the relationships between different time steps.In addition, a collision detection algorithm based on hybrid bounding box is proposed. In this algorithm, collision is pre-judged based on the proposed minimum safe distance and maximum collision distance to avoid creation of an oriented bounding box during collision detection in non-conflict situations, thus improving the efficiency of collision detection while ensuring detection accuracy. The NGSIM dataset is used for model performance verification and the results show that the Root Mean Square Error(RMSE) of the proposed model is lower than that of Conv-LSTM, sys-Conv, and other models, indicating that the trajectory prediction accuracy of the proposed model is higher. Compared with the Oriented Bounding Box(OBB), Axis-Aligned Bounding Box(AABB), and AABB-OBB algorithms, the average collision detection time is reduced by 64.47%, 53.88%, and 55.47% respectively, using the proposed algorithm based on hybrid bounding box.

Key words: trajectory prediction, collision detection, autonomous driving, attention mechanism, intention identification, hybrid bounding box