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

• URL: http://arxiv.org/abs/2505.23129v1
• Date: Thu, 29 May 2025 05:59:24 GMT
• Title: HMAD: Advancing E2E Driving with Anchored Offset Proposals and Simulation-Supervised Multi-target Scoring
• Authors: Bin Wang, Pingjun Li, Jinkun Liu, Jun Cheng, Hailong Lei, Yinze Rong, Huan-ang Gao, Kangliang Chen, Xing Pan, Weihao Gu,
• Abstract summary: We introduce HMAD, a framework integrating a distinctive Bird's-Eye-View (BEV) based trajectory proposal mechanism with learned multi-criteria scoring.<n>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.<n>Demonstrating its efficacy, HMAD achieves a 44.5% driving score on the CVPR 2025 private test set.
• Score: 7.564094719956086
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Tracking the Unstable: Appearance-Guided Motion Modeling for Robust Multi-Object Tracking in UAV-Captured Videos [58.156141601478794]
  Multi-object tracking (UAVT) aims to track multiple objects while maintaining consistent identities across frames of a given video.<n>Existing methods typically model motion cues and appearance separately, overlooking their interplay and resulting in suboptimal tracking performance.<n>We propose AMOT, which exploits appearance and motion cues through two key components: an Appearance-Motion Consistency (AMC) matrix and a Motion-aware Track Continuation (MTC) module.
  arXiv  Detail & Related papers  (2025-08-03T12:06:47Z)
• DriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning [43.284391163049236]
  DriveSuprim is a selection-based paradigm for trajectory selection in autonomous vehicles.<n>It achieves state-of-the-art performance, including collision avoidance and compliance with rules.<n>It maintains high trajectory quality in various driving scenarios.
  arXiv  Detail & Related papers  (2025-06-07T04:39:06Z)
• SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving [51.47621083057114]
  SOLVE is an innovative framework that synergizes Vision-Language Models with end-to-end (E2E) models to enhance autonomous vehicle planning.<n>Our approach emphasizes knowledge sharing at the feature level through a shared visual encoder, enabling comprehensive interaction between VLM and E2E components.
  arXiv  Detail & Related papers  (2025-05-22T15:44:30Z)
• End-to-End Driving with Online Trajectory Evaluation via BEV World Model [52.10633338584164]
  We propose an end-to-end driving framework WoTE, which leverages a BEV World model to predict future BEV states for Trajectory Evaluation.<n>We validate our framework on the NAVSIM benchmark and the closed-loop Bench2Drive benchmark based on the CARLA simulator, achieving state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-04-02T17:47:23Z)
• ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation [44.16465715911478]
  We propose ORION, a holistic E2E autonomous driving framework by vision-language instructed action generation.<n>Our method achieves an impressive closed-loop performance of 77.74 Driving Score (DS) and 54.62% Success Rate (SR) on the challenge Bench2Drive datasets.
  arXiv  Detail & Related papers  (2025-03-25T15:18:43Z)
• DiffAD: A Unified Diffusion Modeling Approach for Autonomous Driving [17.939192289319056]
  We introduce DiffAD, a novel diffusion probabilistic model that redefines autonomous driving as a conditional image generation task.<n>Byizing heterogeneous targets onto a unified bird's-eye view (BEV) and modeling their latent distribution, DiffAD unifies various driving objectives.<n>The reverse process iteratively refines the generated BEV image, resulting in more robust and realistic driving behaviors.
  arXiv  Detail & Related papers  (2025-03-15T15:23:35Z)
• InDRiVE: Intrinsic Disagreement based Reinforcement for Vehicle Exploration through Curiosity Driven Generalized World Model [0.0]
  In this paper, we propose InDRiVE (Intrinsic Disagreement based Reinforcement for Vehicle Exploration) as a model-based Reinforcement Learning framework.<n>By training an ensemble of world models, the agent actively explores high uncertainty regions of environments without task specific feedback.<n> Experimental results in both seen and unseen environments demonstrate that InDRiVE achieves higher success rates and fewer infractions compared to DreamerV2 and DreamerV3 baselines.
  arXiv  Detail & Related papers  (2025-03-07T16:56:00Z)
• Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving [65.61999354218628]
  We take the first step toward designing black-box adversarial attacks specifically targeting vision-language models (VLMs) in autonomous driving systems.<n>We propose Cascading Adversarial Disruption (CAD), which targets low-level reasoning breakdown by generating and injecting semantics.<n>We present Risky Scene Induction, which addresses dynamic adaptation by leveraging a surrogate VLM to understand and construct high-level risky scenarios.
  arXiv  Detail & Related papers  (2025-01-23T11:10:02Z)
• Hybrid Action Based Reinforcement Learning for Multi-Objective Compatible Autonomous Driving [9.39122455540358]
  Reinforcement Learning (RL) has shown excellent performance in solving decision-making and control problems of autonomous driving.<n>Driving is a multi-attribute problem, leading to challenges in achieving multi-objective compatibility for current RL methods.<n>We propose a Multi-objective Ensemble-Critic reinforcement learning method with Hybrid Parametrized Action for multi-objective compatible autonomous driving.
  arXiv  Detail & Related papers  (2025-01-14T13:10:13Z)
• DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving [55.53171248839489]
  We propose an ego-centric fully sparse paradigm, named DiFSD, for end-to-end self-driving.<n>Specifically, DiFSD mainly consists of sparse perception, hierarchical interaction and iterative motion planner.<n>Experiments conducted on nuScenes and Bench2Drive datasets demonstrate the superior planning performance and great efficiency of DiFSD.
  arXiv  Detail & Related papers  (2024-09-15T15:55:24Z)
• Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds [100.61456258283245]
  This paper presents a multi-layer motion planning and control architecture for autonomous racing. The proposed solution has been applied on a Dallara AV-21 racecar and tested at oval race tracks achieving lateral accelerations up to 25 $m/s2$.
  arXiv  Detail & Related papers  (2022-07-22T15:16:54Z)
• Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction [71.97877759413272]
  Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute actions. Recent methods have achieved strong performances using Multi-Choice Learning objectives like winner-takes-all (WTA) or best-of-many. Our work addresses two key challenges in trajectory prediction, learning outputs, and better predictions by imposing constraints using driving knowledge.
  arXiv  Detail & Related papers  (2021-04-16T17:58:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.