Accurate motion forecasting is essential for the safety and reliability of autonomous driving (AD) systems. While existing methods have made significant progress, they often overlook explicit safety constraints and struggle to capture the complex interactions among traffic agents, environmental factors, and motion dynamics. To address these challenges, we present SafeCast, a risk-responsive motion forecasting model that integrates safety-aware decision-making with uncertainty-aware adaptability. SafeCast is the first to incorporate the Responsibility-Sensitive Safety (RSS) framework into motion forecasting, encoding interpretable safety rules--such as safe distances and collision avoidance--based on traffic norms and physical principles. To further enhance robustness, we introduce the Graph Uncertainty Feature (GUF), a graph-based module that injects learnable noise into Graph Attention Networks, capturing real-world uncertainties and enhancing generalization across diverse scenarios. We evaluate SafeCast on four real-world benchmark datasets--Next Generation Simulation (NGSIM), Highway Drone (HighD), ApolloScape, and the Macao Connected Autonomous Driving (MoCAD)--covering highway, urban, and mixed-autonomy traffic environments. Our model achieves state-of-the-art (SOTA) accuracy while maintaining a lightweight architecture and low inference latency, underscoring its potential for real-time deployment in safety-critical AD systems.

翻译：精确的运动预测对于自动驾驶系统的安全性与可靠性至关重要。尽管现有方法已取得显著进展，但往往忽视了显式的安全约束，且难以捕捉交通参与者、环境因素与运动动态之间的复杂交互。为应对这些挑战，我们提出SafeCast——一种集成安全感知决策与不确定性感知适应性的风险响应式运动预测模型。SafeCast首次将责任敏感安全框架融入运动预测，基于交通规则与物理原理编码可解释的安全规则（如安全距离与碰撞规避）。为进一步增强鲁棒性，我们提出图不确定性特征——一种基于图的可学习噪声注入模块，通过图注意力网络捕捉现实世界的不确定性，并提升模型在多样化场景中的泛化能力。我们在四个真实世界基准数据集——下一代仿真、高速公路无人机数据集、ApolloScape及澳门网联自动驾驶数据集——上评估SafeCast，涵盖高速公路、城市及混合自动驾驶交通环境。我们的模型在保持轻量级架构与低推理延迟的同时，实现了最先进的预测精度，彰显了其在安全关键型自动驾驶系统中实时部署的潜力。