用多模态轨迹预测提升驾驶风险评估精度
EDRF: Enhanced Driving Risk Field Based on Multimodal Trajectory Prediction and Its Applications
- 融合深度学习与高斯模型，量化交通参与者行为不确定性
- 通过交互风险指标在多个任务中验证有效性
- 适合自动驾驶安全系统开发与风险预警研究者
驾驶风险评估对自动驾驶和人类驾驶车辆均至关重要。风险可量化为事件发生概率与后果的乘积，但因驾驶员或车辆行为不确定性，事件概率难以准确预测。传统基于运动学的方法常产生不合理的轨迹预测结果。本文提出增强型驾驶风险场（EDRF）模型，结合基于深度学习的多模态轨迹预测结果与高斯分布模型，定量捕捉交通参与者行为的不确定性。同时提出了EDRF的应用方法，通过定义交互风险（IR）概念，应用于交通风险监控、自车风险分析及运动与轨迹规划等任务。每个应用均提供典型场景示例，验证模型有效性。
原文摘要 · Abstract (English)
Driving risk assessment is crucial for both autonomous vehicles and human-driven vehicles. The driving risk can be quantified as the product of the probability that an event (such as collision) will occur and the consequence of that event. However, the probability of events occurring is often difficult to predict due to the uncertainty of drivers' or vehicles' behavior. Traditional methods generally employ kinematic-based approaches to predict the future trajectories of entities, which often yield unrealistic prediction results. In this paper, the Enhanced Driving Risk Field (EDRF) model is proposed, integrating deep learning-based multimodal trajectory prediction results with Gaussian distribution models to quantitatively capture the uncertainty of traffic entities' behavior. The applications of the EDRF are also proposed. It is applied across various tasks (traffic risk monitoring, ego-vehicle risk analysis, and motion and trajectory planning) through the defined concept Interaction Risk (IR). Adequate example scenarios are provided for each application to illustrate the effectiveness of the model.
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