考虑周边车辆潜在风险，提升自动驾驶轨迹预测精度。
STRAP: Spatial-Temporal Risk-Attentive Vehicle Trajectory Prediction for Autonomous Driving
- 引入风险势场模型，捕捉周围车辆的不确定行为风险。
- 在NGSIM和HighD数据集上，高风险场景误差降低31.2%。
- 适合需要安全决策的自动驾驶系统研发者参考。
准确的车辆轨迹预测对保障全自动驾驶系统的安全与效率至关重要。现有方法多关注已观测运动模式及车辆间交互，却常忽略周围车辆不确定或激进行为带来的潜在风险。本文提出一种新的时空风险感知轨迹预测框架，引入风险势场以评估邻近车辆行为引发的感知风险。该框架采用时空编码器与风险感知特征融合解码器，将风险势场嵌入时空特征表示中用于轨迹预测，并设计风险加权损失函数，提升短相对间距等高风险场景的预测准确性。在广泛使用的NGSIM和HighD数据集上的实验表明，相比最先进方法，本方法分别降低平均预测误差4.8%和31.2%，尤其在高风险场景表现显著。所提框架提供可解释的风险感知预测，有助于提升自动驾驶系统的鲁棒性决策能力。
原文摘要 · Abstract (English)
Accurate vehicle trajectory prediction is essential for ensuring safety and efficiency in fully autonomous driving systems. While existing methods primarily focus on modeling observed motion patterns and interactions with other vehicles, they often neglect the potential risks posed by the uncertain or aggressive behaviors of surrounding vehicles. In this paper, we propose a novel spatial-temporal risk-attentive trajectory prediction framework that incorporates a risk potential field to assess perceived risks arising from behaviors of nearby vehicles. The framework leverages a spatial-temporal encoder and a risk-attentive feature fusion decoder to embed the risk potential field into the extracted spatial-temporal feature representations for trajectory prediction. A risk-scaled loss function is further designed to improve the prediction accuracy of high-risk scenarios, such as short relative spacing. Experiments on the widely used NGSIM and HighD datasets demonstrate that our method reduces average prediction errors by 4.8% and 31.2% respectively compared to state-of-the-art approaches, especially in high-risk scenarios. The proposed framework provides interpretable, risk-aware predictions, contributing to more robust decision-making for autonomous driving systems.
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