Latest Autonomous Driving Research Papers

The newest Autonomous Driving papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Autonomous Driving so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• Diffusion Forcing Planner: History-Annealed Planning with Time-Dependent Guidance for Autonomous Driving
  Zehan Zhang, Neng Zhang, Yaoyi Li, Jia Cai et al. · arXiv · Jun 9, 2026

  Learning-based motion planners, despite recent progress, often suffer from temporal inconsistency. Small perturbations across frames can accumulate into unstable trajectories, degrading comfort and safety in closed-loop driving. Several met…

• Language-Driven Cost Optimization for Autonomous Driving
  Diego Martinez-Baselga, Khaled Mustafa, Javier Alonso-Mora · arXiv · Jun 9, 2026

  The driving behavior of autonomous vehicles is typically governed by the cost function of their motion planner, which encodes objectives such as speed tracking, smoothness, lane keeping, and collision avoidance. However, tuning the paramete…

• Resilient Navigation for Autonomous Farm Robots by Leveraging Jerk-Augmented Models with IMU-Only Disturbance Rejection
  Batu Candan, Mohammed Atallah, Simone Servadio, Saeed Arabi · arXiv · Jun 9, 2026

  Precise state estimation for navigation of autonomous agricultural robots is often compromised by sensor outages (GNSS/LiDAR/Visual) and high-frequency vibrations inherent in off-road environments. This paper proposes a robust navigation al…

• An Exposure-Time-Aligned Primary-Path Architecture for Autonomous-Driving ECUs
  Toru Saito, Yuki Hagura, Tatsuya Konishi, Satoru Mizusawa et al. · arXiv · Jun 9, 2026

  While end-to-end (E2E) autonomous driving has become the dominant research direction, production vehicles continue to rely on modular multi-NN pipelines for a non-trivial transitional period. The subject of this paper is the design of an ar…

• Efficient Minimal Solvers for Relative Pose Estimation in Autonomous Driving Applications
  Tao Li, Liang Liu, Jianli Han, Weimin Lv · arXiv · Jun 8, 2026

  With the advancement of visual sensing systems, computer vision is playing an increasingly important role in autonomous driving and robot navigation. Relative pose estimation in multi-camera systems is essential for accurate vehicle localiz…

• Planning-aligned Token Compression for Long-Context Autonomous Driving
  Zhixuan Liang, Yuxiao Chen, Yurong You, Peter Karkus et al. · arXiv · Jun 5, 2026

  Monolithic vision-action models represent an emerging paradigm in autonomous driving. However, this architecture produces token sequences that quickly exceed real-time computational budgets when encoding extended temporal context for comple…

• Re-imagining ISO 26262 in the Age of Autonomous Vehicles: Enhancing Controllability through Transferability and Predictability
  Chaitanya Shinde, Hadi Hajieghrary, Paul Schmitt, Adam Shoemaker et al. · arXiv · Jun 5, 2026

  The ISO 26262 standard defines functional safety for road vehicles through risk assessments based on Severity, Exposure, and Controllability, grounded in a human-driven vehicle paradigm. In the context of autonomous vehicles (AVs), the abse…

• Dash2Sim: Closed-Loop Driving Simulation from in-the-wild Dashcam Videos
  Anurag Ghosh, Francesco Pittaluga, Khiem Vuong, Angela Chen et al. · arXiv · Jun 5, 2026

  Self-driving simulations typically rely on data collected in a small number of cities or on hand-authored synthetic scenarios. Dashcam videos cover a far broader range of locations and situations, including rare or long-tailed scenarios. Th…

• Does Appearance Help? A Systematic Study of Image-Based Re-Identification in Online 3D Multi-Pedestrian Tracking
  Eduardo Borges, Luís Garrote, Urbano J. Nunes · arXiv · Jun 5, 2026

  LiDAR-based 3D Multi-Object Tracking (MOT) typically relies solely on geometric information, which is often insufficient to distinguish between targets during prolonged occlusions or in crowded human-populated environments. While integratin…

• A Causal Probabilistic Framework for Perception-Informed Closed-Loop Simulation of Autonomous Driving
  Zhennan Fei, Rickard Johansson, Mikael Andersson, Matthias Eng et al. · arXiv · Jun 5, 2026

  Software-in-the-loop (SIL) simulation is a cornerstone for the validation of modern automotive safety functions. However, many current frameworks utilize ideal sensing, which bypasses the functional insufficiencies of perception algorithms,…

• Test-Time Trajectory Optimization for Autonomous Driving
  Yihong Xu, Eloi Zablocki, Yuan Yin, Elias Ramzi et al. · arXiv · Jun 5, 2026

  End-to-end planners for autonomous driving typically generate a set of candidate trajectories, score each one, and return the highest-scoring candidate. However, the scorer is applied only after the proposals are generated and cannot influe…

• RiskFlow: Fast and Faithful Safety-Critical Traffic Scenario Generation
  Qi Lan, Yining Tang, Yu Shen, Yi Zhou et al. · arXiv · Jun 4, 2026

  Safety-critical traffic scenario generation is essential for evaluating autonomous driving systems under rare but high-risk interactions. Existing diffusion-based methods offer strong controllability in closed-loop generation, but their ite…

• Waypoints Matter: A Systematic Study for Sampling-Based Trajectory Planning
  Josep M. Barbera, Antonio Artuñedo, Jorge Villagra · arXiv · Jun 4, 2026

  Real-time autonomous driving commonly relies on sampling-based trajectory planners that link candidate trajectories to target waypoints along the road centerline. The placement of these waypoints directly impacts both the existence and qual…

• RadiusFPS: Efficient Farthest Point Sampling on CPUs and GPUs via Spherical Voxel Pruning
  Ziyang Yu, Xiang Li, Qiong Chang, Jun Miyazaki · arXiv · Jun 4, 2026

  Point clouds are a primary sensory representation for robotic perception, underpinning LiDAR-based autonomous driving, simultaneous localization and mapping (SLAM), and navigation. Within these pipelines, Farthest Point Sampling (FPS) is th…

• Breaking Time: A Fully Gaussian Framework for Distributed and Continuous-Time SLAM
  Davide Ceriola, Simone Ferrari, Luca Di Giammarino, Leonardo Brizi et al. · arXiv · Jun 4, 2026

  Continuous-time SLAM provides a principled framework for fusing heterogeneous sensors while estimating smooth trajectories, and is particularly well-suited for handling heterogeneous, asynchronous sensor streams with non-uniform readout pat…

• CLEAR: Cognition and Latent Evaluation for Adaptive Routing in End-to-End Autonomous Driving
  Yining Xing, Zehong Ke, Zhiyuan Liu, Yanbo Jiang et al. · arXiv · Jun 4, 2026

  End-to-end autonomous driving models often struggle to balance multi-modal maneuver generation with real-time inference constraints. While diffusion models successfully capture diverse driving behaviors, their iterative denoising process in…

• Towards Realistic 3D Sonar Simulation
  Youssef Attia, Davide Costa, Francesco Wanderlingh, Filippo Campagnaro et al. · arXiv · Jun 4, 2026

  As underwater robotics research increasingly addresses complex 3D perception and autonomous navigation, the fidelity of sonar simulation has become a key factor in algorithm development. Current simulation frameworks typically rely on geome…

• Not All Points Are Equal: Uncertainty-Aware 4D LiDAR Scene Synthesis
  Xiang Xu, Alan Liang, Youquan Liu, Xian Sun et al. · arXiv · Jun 1, 2026

  Constructing faithful 4D worlds from LiDAR-acquired sequences is crucial for embodied AI, yet current generative frameworks apply uniform modeling capacity across all spatial regions. This ignores that perceptual difficulty varies dramatica…

• SARAD: LLM-Based Safety-Aware Hybrid Reinforcement Learning with Collision Prediction for Autonomous Driving
  Kangyu Wu, Peng Cui, Guoxi Chen, Ya Zhang · arXiv · May 27, 2026

  Ensuring both safety and efficiency in decision-making for autonomous driving systems remains a fundamental challenge. Traditional Deep Reinforcement Learning (DRL) suffers from unsafe random exploration and slow convergence, while Large La…

• AnyScene: Towards Highly Controllable Driving Scene Generation at Anywhere and Beyond
  Haiming Zhang, Junfei Zhou, Feng Jiang, Jingzhong Li et al. · arXiv · May 25, 2026

  Generating high-fidelity and controllable synthetic data is critical for advancing end-to-end autonomous driving, particularly for addressing the long tail of rare safety-critical scenarios. Existing occupancy-guided methods typically rely …

• Branch-Stochastic Model Predictive Control for Motion Planning under Multi-Modal Uncertainty with Scenario Clustering
  Zekun Xing, Ramkrishna Chaudhari, Marion Leibold, Dirk Wollherr et al. · arXiv · May 21, 2026

  Motion planning for autonomous driving must account for multi-modal uncertainty in both the intentions and trajectories of surrounding vehicles. Handling uncertainty in a worst-case manner guarantees robustness but often leads to excessive …

• Diffusion-guided Generalizable Enhancer for Urban Scene Reconstruction
  Henry Che, Jingkang Wang, Yun Chen, Ze Yang et al. · arXiv · May 21, 2026

  Urban scene reconstruction from real-world observations has emerged as a powerful tool for self-driving development and testing. While current neural rendering approaches achieve high-fidelity rendering along the recorded trajectories, thei…

• StableVLA: Towards Robust Vision-Language-Action Models without Extra Data
  Yiyang Fu, Chubin Zhang, Shukai Gong, Yufan Deng et al. · arXiv · May 18, 2026

  It is infeasible to encompass all possible disturbances within the training dataset. This raises a critical question regarding the robustness of Vision-Language-Action (VLA) models when encountering unseen real-world visual disturbances, pa…

• RGB-only Active 3D Scene Graph Generation for Indoor Mobile Robots
  Giorgia Modi, Davide Buoso, Giuseppe Averta, Daniele De Martini · arXiv · May 18, 2026

  Current approaches to 3D scene graph generation rely on dedicated depth sensors, such as LiDAR or RGB-D cameras, for metric 3D reconstruction. This limits deployment to specialized robotic platforms and excludes settings where only RGB came…

• CLOVER: Closed-Loop Value Estimation \& Ranking for End-to-End Autonomous Driving Planning
  Sining Ang, Yuguang Yang, Canyu Chen, Yan Wang · arXiv · May 14, 2026

  End-to-end autonomous driving planners are commonly trained by imitating a single logged trajectory, yet evaluated by rule-based planning metrics that measure safety, feasibility, progress, and comfort. This creates a training--evaluation m…

• SOCC-ICP: Semantics-Assisted Odometry based on Occupancy Grids and ICP
  Johannes Scherer, Sebastian Hirt, Henri Meeß · arXiv · May 14, 2026

  Reliable pose estimation in previously unseen environments is a fundamental capability of autonomous systems. Existing LiDAR odometry methods typically employ point-, surfel-, or NDT-based map representations, which are distinct from the se…

• FU-MPC: Frontier- and Uncertainty-Aware Model Predictive Control for Efficient and Accurate UAV Exploration with Motorized LiDAR
  Jianping Li, Pengfei Wan, Zhongyuan Liu, Yi Wang et al. · arXiv · May 14, 2026

  Efficient UAV exploration in unknown environments requires rapid coverage expansion while maintaining accurate and reliable localization, since safe navigation in complex scenes depends on consistent mapping and pose estimation. However, fo…

• Learning Direct Control Policies with Flow Matching for Autonomous Driving
  Marcello Ceresini, Federico Pirazzoli, Andrea Bertogalli, Lorenzo Cipelli et al. · arXiv · May 14, 2026

  We present a flow-matching planner for autonomous driving that directly outputs actionable control trajectories defined by acceleration and curvature profiles. The model is conditioned on a bird's-eye-view (BEV) raster of the surrounding sc…

• TriBand-BEV: Real-Time LiDAR-Only 3D Pedestrian Detection via Height-Aware BEV and High-Resolution Feature Fusion
  Mohammad Khoshkdahan, Alexey Vinel · arXiv · May 12, 2026

  Safe autonomous agents and mobile robots need fast real time 3D perception, especially for vulnerable road users (VRUs) such as pedestrians. We introduce a new bird's eye view (BEV) encoding, which maps the full 3D LiDAR point cloud into a …

• MDrive: Benchmarking Closed-Loop Cooperative Driving for End-to-End Multi-agent Systems
  Marco Coscoy, Zewei Zhou, Seth Z. Zhao, Henry Wei et al. · arXiv · May 11, 2026

  Vehicle-to-Everything (V2X) communication has emerged as a promising paradigm for autonomous driving, enabling connected agents to share complementary perception information and negotiate with each other to benefit the final planning. Exist…