SUPER-AD: Semantic Uncertainty-aware Planning for End-to-End Robust Autonomous Driving

• URL: http://arxiv.org/abs/2511.22865v1
• Date: Fri, 28 Nov 2025 03:50:44 GMT
• Title: SUPER-AD: Semantic Uncertainty-aware Planning for End-to-End Robust Autonomous Driving
• Authors: Wonjeong Ryu, Seungjun Yu, Seokha Moon, Hojun Choi, Junsung Park, Jinkyu Kim, Hyunjung Shim,
• Abstract summary: We propose a camera-only E2E framework that estimates aleatoric uncertainty directly in BEV space and incorporates it into planning.<n>Our method produces a dense, uncertainty-aware drivability map that captures both semantic structure and geometric layout at pixel-level resolution.
• Score: 36.91878828972102
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: End-to-End (E2E) planning has become a powerful paradigm for autonomous driving, yet current systems remain fundamentally uncertainty-blind. They assume perception outputs are fully reliable, even in ambiguous or poorly observed scenes, leaving the planner without an explicit measure of uncertainty. To address this limitation, we propose a camera-only E2E framework that estimates aleatoric uncertainty directly in BEV space and incorporates it into planning. Our method produces a dense, uncertainty-aware drivability map that captures both semantic structure and geometric layout at pixel-level resolution. To further promote safe and rule-compliant behavior, we introduce a lane-following regularization that encodes lane structure and traffic norms. This prior stabilizes trajectory planning under normal conditions while preserving the flexibility needed for maneuvers such as overtaking or lane changes. Together, these components enable robust and interpretable trajectory planning, even under challenging uncertainty conditions. Evaluated on the NAVSIM benchmark, our method achieves state-of-the-art performance, delivering substantial gains on both the challenging NAVHARD and NAVSAFE subsets. These results demonstrate that our principled aleatoric uncertainty modeling combined with driving priors significantly advances the safety and reliability of camera-only E2E autonomous driving.

Related papers

• Conformal Reachability for Safe Control in Unknown Environments [29.315278038378835]
  We develop a probabilistic verification framework for unknown dynamical systems.<n>We use conformal prediction to obtain valid uncertainty intervals for the unknown dynamics at each time step.<n>We also develop an algorithmic approach for training control policies that optimize nominal reward while also maximizing the planning horizon.
  arXiv  Detail & Related papers  (2026-02-03T18:01:38Z)
• Assistive Decision-Making for Right of Way Navigation at Uncontrolled Intersections [0.17205106391379021]
  Uncontrolled intersections account for a significant fraction of roadway crashes due to ambiguous right-of-way rules.<n>We present a driver-assist framework for right-of-way reasoning at uncontrolled intersections, formulated as a Partially Observable Markov Decision Process.<n>Probability planners outperform the rule-based baseline, achieving up to 97.5 percent collision-free navigation under partial observability.
  arXiv  Detail & Related papers  (2025-09-22T20:46:23Z)
• FlowDrive: Energy Flow Field for End-to-End Autonomous Driving [50.89871153094958]
  FlowDrive is a novel framework that introduces physically interpretable energy-based flow fields to encode semantic priors and safety cues into the BEV space.<n> Experiments on the NAVSIM v2 benchmark demonstrate that FlowDrive achieves state-of-the-art performance with anS of 86.3, surpassing prior baselines in both safety and planning quality.
  arXiv  Detail & Related papers  (2025-09-17T13:51:33Z)
• ImagiDrive: A Unified Imagination-and-Planning Framework for Autonomous Driving [64.12414815634847]
  Vision-Language Models (VLMs) and Driving World Models (DWMs) have independently emerged as powerful recipes addressing different aspects of this challenge.<n>We propose ImagiDrive, a novel end-to-end autonomous driving framework that integrates a VLM-based driving agent with a DWM-based scene imaginer.
  arXiv  Detail & Related papers  (2025-08-15T12:06:55Z)
• 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)
• Plan-R1: Safe and Feasible Trajectory Planning as Language Modeling [74.41886258801209]
  We propose a two-stage trajectory planning framework that decouples principle alignment from behavior learning.<n>Plan-R1 significantly improves planning safety and feasibility, achieving state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-05-23T09:22:19Z)
• SafeCast: Risk-Responsive Motion Forecasting for Autonomous Vehicles [12.607007386467329]
  We present SafeCast, a risk-responsive motion forecasting model.<n>It integrates safety-aware decision-making with uncertainty-aware adaptability.<n>Our model achieves state-of-the-art (SOTA) accuracy while maintaining a lightweight architecture and low inference latency.
  arXiv  Detail & Related papers  (2025-03-28T15:38:21Z)
• Differentiable Control Barrier Functions for Vision-based End-to-End Autonomous Driving [100.57791628642624]
  We introduce a safety guaranteed learning framework for vision-based end-to-end autonomous driving. We design a learning system equipped with differentiable control barrier functions (dCBFs) that is trained end-to-end by gradient descent.
  arXiv  Detail & Related papers  (2022-03-04T16:14:33Z)
• 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)
• Online Mapping and Motion Planning under Uncertainty for Safe Navigation in Unknown Environments [3.2296078260106174]
  This manuscript proposes an uncertainty-based framework for mapping and planning feasible motions online with probabilistic safety-guarantees. The proposed approach deals with the motion, probabilistic safety, and online computation constraints by: (i) mapping the surroundings to build an uncertainty-aware representation of the environment, and (ii) iteratively (re)planning to goal that are kinodynamically feasible and probabilistically safe through a multi-layered sampling-based planner in the belief space.
  arXiv  Detail & Related papers  (2020-04-26T08:53:37Z)

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.