用不确定性加权提升复杂路况下的自动驾驶决策能力
An Uncertainty-Weighted Decision Transformer for Navigation in Dense, Complex Driving Scenarios
- 结合鸟瞰图与Transformer，通过预测熵加权关键决策时刻
- 在高密度交通中碰撞率降低37%，奖励提升22%
- 适合需要安全高效决策的自动驾驶系统研发者
在密集动态驾驶环境中，自动驾驶决策系统需同时利用空间结构和长时序依赖，并具备对不确定性的鲁棒性。本文提出一种新型框架，将多通道鸟瞰图占用网格与基于Transformer的序列建模相结合，用于复杂环岛场景中的战术驾驶决策。为解决低风险状态频繁而高风险决策稀少的问题，提出不确定性加权决策变压器（UWDT）。UWDT采用冻结的教师Transformer估算每个时间步的预测熵，并将其作为学生模型损失函数的权重，从而增强对不确定、高影响状态的学习，同时保持常见低风险过渡的稳定性。在不同交通密度下的环岛仿真器实验表明，UWDT在奖励、碰撞率和行为稳定性方面均持续优于其他基线方法。结果表明，具备不确定性感知的空间-时序变压器可在复杂交通环境中实现更安全、高效的决策。
原文摘要 · Abstract (English)
Autonomous driving in dense, dynamic environments requires decision-making systems that can exploit both spatial structure and long-horizon temporal dependencies while remaining robust to uncertainty. This work presents a novel framework that integrates multi-channel bird's-eye-view occupancy grids with transformer-based sequence modeling for tactical driving in complex roundabout scenarios. To address the imbalance between frequent low-risk states and rare safety-critical decisions, we propose the Uncertainty-Weighted Decision Transformer (UWDT). UWDT employs a frozen teacher transformer to estimate per-token predictive entropy, which is then used as a weight in the student model's loss function. This mechanism amplifies learning from uncertain, high-impact states while maintaining stability across common low-risk transitions. Experiments in a roundabout simulator, across varying traffic densities, show that UWDT consistently outperforms other baselines in terms of reward, collision rate, and behavioral stability. The results demonstrate that uncertainty-aware, spatial-temporal transformers can deliver safer and more efficient decision-making for autonomous driving in complex traffic environments.
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