HMAD: Advancing E2E Driving with Anchored Offset Proposals and Simulation-Supervised Multi-target Scoring

2025-05-29

AI Summary

End-to-end autonomous driving faces two key challenges: insufficient trajectory diversity and weak safety evaluation. To address these, we propose a generation-evaluation decoupled framework. First, we generate a large set of compliant and stable candidate trajectories via iterative offset decoding, leveraging BEVFormer and learnable anchor queries. Second, we introduce a simulation-driven multi-objective scoring module that jointly optimizes fault-free collision rate, drivable-area coverage, ride comfort, and an extended Planning Decision Metric (PDM). Our method pioneers an anchor-based offset trajectory proposal mechanism and a multi-task scorer network. Evaluated on the CVPR 2025 private test set, it achieves a 44.5% driving score—significantly outperforming baseline methods. This paradigm synergistically enhances both trajectory generation quality and evaluation robustness, establishing a novel, interpretable, and verifiable approach to safe path selection for end-to-end driving decision-making.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Security and Privacy: Large-scale security measurements

Abstract

End-to-end autonomous driving faces persistent challenges in both generating diverse, rule-compliant trajectories and robustly selecting the optimal path from these options via learned, multi-faceted evaluation. To address these challenges, we introduce HMAD, a framework integrating a distinctive Bird's-Eye-View (BEV) based trajectory proposal mechanism with learned multi-criteria scoring. HMAD leverages BEVFormer and employs learnable anchored queries, initialized from a trajectory dictionary and refined via iterative offset decoding (inspired by DiffusionDrive), to produce numerous diverse and stable candidate trajectories. A key innovation, our simulation-supervised scorer module, then evaluates these proposals against critical metrics including no at-fault collisions, drivable area compliance, comfortableness, and overall driving quality (i.e., extended PDM score). Demonstrating its efficacy, HMAD achieves a 44.5% driving score on the CVPR 2025 private test set. This work highlights the benefits of effectively decoupling robust trajectory generation from comprehensive, safety-aware learned scoring for advanced autonomous driving.

Problem

• Generating diverse, rule-compliant autonomous driving trajectories
• Robustly selecting optimal path via multi-criteria learned scoring
• Decoupling trajectory generation from safety-aware scoring for E2E driving

Innovation

• BEV-based trajectory proposal mechanism
• Simulation-supervised multi-criteria scoring
• Anchored queries with iterative offset decoding