TriDrive：面向预测与驾驶员监控的驾驶员、车辆与道路联合建模
TriDrive: Joint Driver, Vehicle, and Road Modeling for Forecasting and Driver Monitoring
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中文总结 AI 辅助
TriDrive是首个联合预测驾驶员、车辆和道路的统一框架，通过自动化条件转移模型和有向残差连接提升预测性能，并在实时车载评估中优于基线。
中文摘要 AI 辅助
预测驾驶员、车辆和道路场景如何相互作用并共同演化是驾驶员监控的核心。以往的工作孤立地建模座舱内活动或交通条件下的驾驶员运动，这促使我们开展驾驶员、车辆和道路的联合建模，并进行实时车载评估。我们提出TriDrive，据我们所知，这是首个通过自动化条件转移模型联合预测驾驶员运动学、车辆动力学和道路需求的统一框架。各模态专用编码器（驾驶员的锚定运动学表示、因果CAN总线动力学、以及带有结构化道路边界的冻结V-JEPA 2道路潜在特征）通过有向残差连接相连，驾驶员和道路上下文通过这些连接细化车辆预测。我们在三个下游任务上评估TriDrive。在公开的AIDE基准上，其运动学编码器方案在已发表的基线中创下了新的全集最先进水平（SOTA）（All-MPJPE 48.05对71.47）。在197.2小时的自然驾驶BATON子集上，有向连接和道路边界将辅助接管的PR-AUC提升了0.084（转向起始）和0.286（碰撞时间下降）。对于实时使用，我们蒸馏道路编码器，并在带有外部8 GB GPU的comma four上运行TriDrive，其中轻量级当前状态警告探针以5 Hz更新，p95延迟为177毫秒，同时联合模型并发预测。该探针在人工标注的手动驾驶警告上优于基于openpilot的基线（AUROC 0.725对0.563），在一项配对道路研究中，14名驾驶员认为其警告比openpilot驾驶员监控系统的警告更合适（+1.79）和更及时（+2.67）。
英文摘要
Predicting how drivers, vehicles, and road scenes interact and evolve together is central to driver monitoring. Prior work models in-cabin activity or traffic-conditioned driver motion in isolation, motivating joint driver, vehicle, and road modeling with real-time on-vehicle evaluation. We introduce TriDrive, to our knowledge the first unified framework that jointly forecasts driver kinematics, vehicle dynamics, and road demands through an automation-conditioned transition model. Modality-specific encoders (an anchored kinematic representation of the driver, causal CAN-bus dynamics, and frozen V-JEPA 2 road latents with structured road margins) are connected by directed residual connections through which driver and road context refine vehicle forecasts. We evaluate TriDrive on three downstream tasks. On the public AIDE benchmark, its kinematic encoder recipe sets a new full-set state of the art (SOTA) among published baselines (48.05 versus 71.47 All-MPJPE). On 197.2 hours of naturalistic BATON subset, directed connections and road margins raise assistance-engaged PR-AUC by 0.084 for steering onset and 0.286 for time-to-collision drops. For real-time use, we distill the road encoders and run TriDrive on a comma four with an external 8 GB GPU, where a lightweight current-state warning probe updates at 5 Hz with 177 ms p95 latency while the joint model forecasts concurrently. The probe is above an openpilot-based baseline on human-labeled manual-driving warnings (AUROC 0.725 versus 0.563), and in a paired on-road study 14 drivers rate its warnings as more appropriate (+1.79) and timely (+2.67) than those of openpilot's driver-monitoring system.
