An Uncertainty-Weighted Decision Transformer for Navigation in Dense, Complex Driving Scenarios

• URL: http://arxiv.org/abs/2509.13132v1
• Date: Tue, 16 Sep 2025 14:48:52 GMT
• Title: An Uncertainty-Weighted Decision Transformer for Navigation in Dense, Complex Driving Scenarios
• Authors: Zhihao Zhang, Chengyang Peng, Minghao Zhu, Ekim Yurtsever, Keith A. Redmill,
• Abstract summary: This work presents a novel framework that integrates multi-channel bird's-eye-view occupancy grids with transformer-based sequence modeling.<n>We propose the Uncertainty-Weighted Decision Transformer (UWDT) to address the imbalance between frequent low-risk states and rare safety-critical decisions.
• Score: 6.059385717057299
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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