通过动态风险时域分析，提升自动驾驶轨迹预测的准确性与安全性。
From Cues to Horizons: Dynamic Risk Horizon Profiling for Trajectory Prediction
- 引入可学习的风险时域轮廓模块，实时追踪周围物体的未来风险演变。
- 在highD和SHRP2数据集上分别降低25.0%的5秒RMSE和29.1%的最小前向距离误差。
- 适合需要高安全性的自动驾驶路径规划与智能辅助系统研发者使用。
准确可靠的车辆轨迹预测对自动驾驶安全至关重要。现有研究虽已将安全风险纳入轨迹预测以量化周边交通参与者带来的危险，但多数方法仅将历史风险作为辅助信号，忽略了风险的未来演化过程与不确定性。本文提出风险时域轮廓（RHP）模块，采用连续可学习的势场模型实现风险感知的轨迹预测。该模块通过计算周围物体在时空上的接近程度，对未来的风险分布进行建模，从而自适应地识别出人类驾驶员认为的关键时刻。我们在两个不同驾驶场景的数据集上进行了评估：highD（高速公路）和SHRP2（城市道路），涵盖安全、近碰撞及碰撞等多种风险场景。相比基线方法，本框架在highD数据集上5秒均方根误差降低25.0%，在SHRP2数据集上最小前向距离误差降低29.1%。结果表明，该方法在短时与长时轨迹预测中表现优异，并在高速公路与城市环境间具有强泛化能力。所提方法有助于实现更真实的自动驾驶路径规划与策略选择，推动更安全的自动驾驶与高级驾驶辅助系统发展。源代码见：https://github.com/bilab-nyu/RHP。
原文摘要 · Abstract (English)
Accurate and reliable vehicle trajectory prediction is essential for safe autonomous driving. Recent studies have incorporated safety risk into trajectory prediction to quantify dangers posed by surrounding agents. However, most risk-aware approaches use past risk information as a secondary signal to help guide decisions, overlooking its future evolution and uncertainty. In this paper, we propose a risk horizon profiling (RHP) module that incorporates a continuous, learnable potential field model for risk-aware trajectory prediction. The RHP module calculates the spatial-temporal proximity of surrounding objects to profile risk distributions across future horizons, which supports better trajectory prediction by adaptively identifying what human drivers perceive as critical moments. We evaluate our method on two datasets from different driving settings, highD for highway corridors and SHRP2 for urban streets, which cover diverse risk scenarios including safe, near-crash, and crash events. Compared to the baseline methods, our framework achieves a 25.0\% reduction in 5s RMSE on the highD dataset and a 29.1\% reduction in 5s minFDE on SHRP2. These results indicate strong performance for both short and long horizon prediction and robust generalization across highway and urban scenarios. The proposed method enables more realistic AV path planning and strategic selection, thereby supporting safer autonomous driving and more advanced driver-assistance systems. The source code for this work is available at: https://github.com/bilab-nyu/RHP
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。