DriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning

• URL: http://arxiv.org/abs/2506.06659v2
• Date: Sun, 22 Jun 2025 03:57:46 GMT
• Title: DriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning
• Authors: Wenhao Yao, Zhenxin Li, Shiyi Lan, Zi Wang, Xinglong Sun, Jose M. Alvarez, Zuxuan Wu,
• Abstract summary: 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.
• Score: 43.284391163049236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In complex driving environments, autonomous vehicles must navigate safely. Relying on a single predicted path, as in regression-based approaches, usually does not explicitly assess the safety of the predicted trajectory. Selection-based methods address this by generating and scoring multiple trajectory candidates and predicting the safety score for each, but face optimization challenges in precisely selecting the best option from thousands of possibilities and distinguishing subtle but safety-critical differences, especially in rare or underrepresented scenarios. We propose DriveSuprim to overcome these challenges and advance the selection-based paradigm through a coarse-to-fine paradigm for progressive candidate filtering, a rotation-based augmentation method to improve robustness in out-of-distribution scenarios, and a self-distillation framework to stabilize training. DriveSuprim achieves state-of-the-art performance, reaching 93.5% PDMS in NAVSIM v1 and 87.1% EPDMS in NAVSIM v2 without extra data, demonstrating superior safetycritical capabilities, including collision avoidance and compliance with rules, while maintaining high trajectory quality in various driving scenarios.

Related papers

• SafePath: Conformal Prediction for Safe LLM-Based Autonomous Navigation [67.22657932549723]
  We introduce SafePath, a framework that augments Large Language Models (LLMs) with formal safety guarantees.<n>In the first stage, we use an LLM that generates a set of diverse candidate paths, exploring possible trajectories based on agent behaviors and environmental cues.<n>In the second stage, SafePath filters out high-risk trajectories while guaranteeing at least one safe option is included with a user-defined probability.<n>In the final stage, our approach selects the path with the lowest expected collision risk when uncertainty is low or delegates control to a human when uncertainty is high.
  arXiv  Detail & Related papers  (2025-05-14T14:28:24Z)
• Boundary-Guided Trajectory Prediction for Road Aware and Physically Feasible Autonomous Driving [11.436186697804835]
  This paper proposes a novel framework that formulates trajectory prediction as a constrained regression guided by permissible driving directions and their boundaries.<n>We evaluate our approach on the Argoverse-2 dataset against the HPTR baseline.
  arXiv  Detail & Related papers  (2025-05-10T19:21:00Z)
• Minds on the Move: Decoding Trajectory Prediction in Autonomous Driving with Cognitive Insights [18.92479778025183]
  In driving scenarios, a vehicle's trajectory is determined by the decision-making process of human drivers.<n>Previous models fail to capture the true intentions of human drivers, leading to suboptimal performance in long-term trajectory prediction.<n>We introduce a Cognitive-Informed Transformer (CITF) that incorporates a cognitive concept, Perceived Safety, to interpret drivers' decision-making mechanisms.
  arXiv  Detail & Related papers  (2025-02-27T13:43:17Z)
• A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses [68.68514648185828]
  Trajectory prediction is essential for the safety and efficiency of planning in autonomous vehicles.<n>Current models often fail to fully capture complex traffic rules and the complete range of potential vehicle movements.<n>This study introduces three novel loss functions: Offroad Loss, Direction Consistency Error, and Diversity Loss.
  arXiv  Detail & Related papers  (2024-11-29T14:47:08Z)
• An End-to-End Smart Predict-then-Optimize Framework for Vehicle Relocation Problems in Large-Scale Vehicle Crowd Sensing [10.74565749809106]
  Vehicle systems often exhibit biased coverage due to the nature of trip requests and routes.<n>We develop an end-to-end Smart Predict-then-optize (SPO) framework by integrating optimization into prediction.<n>The framework is trained by task-specific matching rather than the upstream prediction error.
  arXiv  Detail & Related papers  (2024-11-27T15:16:22Z)
• Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation [5.928213664340974]
  This research addresses the safety issue in the control optimization problem of autonomous driving. We propose a novel, model-based approach for policy optimization, utilizing a conditional Value-at-Risk based Soft Actor Critic. Our method introduces a worst-case actor to guide safe exploration, ensuring rigorous adherence to safety requirements even in unpredictable scenarios.
  arXiv  Detail & Related papers  (2024-07-08T18:32:40Z)
• Integrating Higher-Order Dynamics and Roadway-Compliance into Constrained ILQR-based Trajectory Planning for Autonomous Vehicles [3.200238632208686]
  Trajectory planning aims to produce a globally optimal route for Autonomous Passenger Vehicles. Existing implementations utilizing the vehicle bicycle kinematic model may not guarantee controllable trajectories. We augment this model by higher-order terms, including the first and second-order derivatives of curvature and longitudinal jerk.
  arXiv  Detail & Related papers  (2023-09-25T22:30:18Z)
• Meta-Learning Priors for Safe Bayesian Optimization [72.8349503901712]
  We build on a meta-learning algorithm, F-PACOH, capable of providing reliable uncertainty quantification in settings of data scarcity. As core contribution, we develop a novel framework for choosing safety-compliant priors in a data-riven manner. On benchmark functions and a high-precision motion system, we demonstrate that our meta-learned priors accelerate the convergence of safe BO approaches.
  arXiv  Detail & Related papers  (2022-10-03T08:38:38Z)
• 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)
• The Importance of Prior Knowledge in Precise Multimodal Prediction [71.74884391209955]
  Roads have well defined geometries, topologies, and traffic rules. In this paper we propose to incorporate structured priors as a loss function. We demonstrate the effectiveness of our approach on real-world self-driving datasets.
  arXiv  Detail & Related papers  (2020-06-04T03:56:11Z)

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.