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

• Development of a New Intelligent Algorithm to Improve Autonomous Car Operation
  Reda, Mohamed · CLOK (University of Central... · Jan 1, 2027

  Autonomous Driving Systems (ADS) are transforming modern transportation by enabling safer, more efficient vehicle operation. Among their core components, local path planning remains a significant challenge due to the need for optimal naviga…

• Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections
  Gil Lifshits, Igal Bilik, Gilad Katz · arXiv · Jul 23, 2026

  Coordinating autonomous vehicles at unsignalized intersections remains a critical challenge for multi-agent reinforcement learning (MARL) systems, which typically struggle with combinatorial action spaces, reliance on privileged information…

• Factorized Spatio-Temporal Convolutions for Human Pose Estimation from Planar Lidar
  Simone Arreghini, Mirko Nava, Nicholas Carlotti, Antonio Paolillo et al. · arXiv · Jul 23, 2026

  Localizing nearby humans and estimating their facing direction are key capabilities for safe navigation and socially aware human-robot interaction. Many pose-estimation pipelines target cameras and 3D LiDAR or assume GPU-class compute, wher…

• HGeo-TopoMap: Boosting Topological Mapping with Hierarchical Geometric Priors
  Siyu Li, Kunyu Peng, Di Wen, Beiping Hou et al. · arXiv · Jul 23, 2026

  Topological maps are key outputs of autonomous driving perception systems, delivering essential road information for path planning. They identify instances such as centerlines and traffic signs, along with their connectivity relationships. …

• A Real-Time Generalized Nash Equilibrium Framework for Interaction-Aware Autonomous Driving in Mixed Traffic
  Nouhed Naidja, Mohamed-Cherif Rahal, Steve Pechberti, Stéphane Font et al. · arXiv · Jul 23, 2026

  Safe and efficient navigation in mixed-traffic environments remains a critical challenge for Autonomous Vehicles (AVs), primarily due to the complex interdependence between the AV's decisions and the unpredictable reactions of human drivers…

• Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving
  Nuoran Li, Zhang Zhang, Yueran Zhao, Tianze Wang et al. · arXiv · Jul 22, 2026

  Vehicle-to-everything-aided autonomous driving (V2X-AD) significantly enhances driving performance through information sharing. However, existing collaborative perception methods only optimize module-level perception capabilities and fail t…

• Cognitive Dual-Process Planning for Autonomous Driving with Structured Scene Knowledge and Verifiable Reasoning-Action Consistency
  Zhongyao Yang, Haoyu Li, Yu Yan, Zhuangxuan Yu et al. · arXiv · Jul 21, 2026

  High-level planning for autonomous driving is a knowledge-intensive engineering decision task that requires accurate scene understanding, timely inference, and internally consistent action selection. Vision-language models (VLMs) can make i…

• Decomposing decisions: causal concept explanations for deep learning models
  Muhammad Imran Khalid, Mi Jian-Xun · Figshare · Jul 21, 2026

  The deployment of deep learning models in high-stakes applications such as autonomous driving is critically important, yet their black-box nature remains a fundamental barrier to trust and accountability. Existing explainability methods typ…

• Safety-Aware Event-Triggered Intervention for Motion Planning and Decision Making in Diffusion-Based Autonomous Driving
  Xuerui Fang, Hui Li, Zehao Xue, Gulimila Kezierbieke · Big Data and Cognitive Comp... · Jul 21, 2026

  Diffusion-based trajectory planners achieve strong nominal performance in autonomous driving, but sparse safety intervention remains difficult to evaluate and realize effectively. This study addresses this problem by proposing a safety-awar…

• Decomposing decisions: causal concept explanations for deep learning models
  Muhammad Imran Khalid, Mi Jian-Xun · Figshare · Jul 21, 2026

  The deployment of deep learning models in high-stakes applications such as autonomous driving is critically important, yet their black-box nature remains a fundamental barrier to trust and accountability. Existing explainability methods typ…

• Kernel-Based Learning of Safety Barriers
  Oliver Schön, Zhengang Zhong, Sadegh Soudjani · Journal of Artificial Intel... · Jul 20, 2026

  The rapid integration of AI algorithms in safety-critical applications such as autonomous driving and healthcare is raising significant concerns about the ability to meet stringent safety standards. Traditional tools for formal safety verif…

• Bridging the gap: A low-cost, ROS-based testbed for rapid prototyping of autonomous outdoor navigation
  Rabah Louali, Samir Sakhi, Kalamai Burinyuy Mbulai, Roumaissa Linda Chir et al. · Proceedings of the Institut... · Jul 20, 2026

  The development and validation of autonomous vehicle (AV) systems remain complex, costly, and high-risk endeavors. While simulations suffer from a “reality gap” and full-scale vehicle tests are prohibitively expensive, the lack of intermedi…

• YOLOv8n-FEL: A Lightweight Visual Detector with a Progressive Compress-Then-Enhance Architecture for Autonomous Driving
  Lei Li, Xingrong Cheng, Xiaofeng Yin, Runyu Mao · Mathematics · Jul 20, 2026

  Real-time visual object detection on resource-constrained in-vehicle platforms requires a practical balance among detection accuracy, model size, computational complexity, and inference speed. Existing lightweight detectors may sacrifice ac…

• UA-CF: Uncertainty-Aware Camera-LiDAR Fusion for Robust 3D Object Detection Under Adverse Weather
  Ana Torres, Ilya Morozov, Grace Turner · Journal of Mathematical Fin... · Jul 20, 2026

  Robust 3D object detection remains a critical bottleneck for autonomous driving under fog, rain, and snow because camera and LiDAR streams do not fail in the same way. Cameras provide dense semantic cues but lose contrast under scattering a…

• WeatherPrompt-Fusion: Prompt-Guided Multi-Modal Perception for Autonomous Driving in Adverse Weather
  Leila Mansour · International Journal of Ad... · Jul 20, 2026

  Reliable autonomous driving perception remains difficult in adverse weather because camera appearance, LiDAR point density, and radar responses degrade in different and condition-dependent ways. Inspired by recent gated-vision and LiDAR fus…

• Behavioral fingerprints: driver profiling using transformer models on next generation simulation trajectory data
  Mohamed Laamimach, Mghari Mohammed, Aziz Mabrouk · TELKOMNIKA (Telecommunicati... · Jul 19, 2026

  Characterizing individual driver behavior is essential for advancing intelligent transportation systems (ITS) and autonomous vehicle safety. While deep learn ing models excel at macroscopic traffic prediction, individual driving styles are …

• TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale
  Zhouchonghao Wu, Akshay Rangesh, Weixin Li, Wei-Jer Chang et al. · arXiv · Jul 14, 2026

  Training robust autonomous driving agents requires a simulator that is fast enough for reinforcement learning at scale, realistic enough to ground behavior in real-world map structure, and diverse enough to cover the safety-critical long ta…

• Improving Autonomous Nano-drones Performance via Automated End-to-End Optimization and Deployment of DNNs
  Vlad Niculescu, Lorenzo Lamberti, Francesco Conti, Luca Benini et al. · arXiv · Jul 14, 2026

  The evolution of energy-efficient ultra-low-power (ULP) parallel processors and the diffusion of convolutional neural networks (CNNs) are fueling the advent of autonomous driving nano-sized unmanned aerial vehicles (UAVs). These sub-10 cm r…

• ERSGNet: An Edge-Guided Residual Spatial Gating Fusion Network for Real-Time Semantic Segmentation
  Zheming Wu, Lin Jiang, Tete Wang · Machines · Jul 14, 2026

  With the development of autonomous driving and general real-time vision applications, semantic segmentation is increasingly expected to provide reliable pixel-level scene understanding under strict latency and computational constraints. Exi…

• UGBCF-Net: Uncertainty-Guided BEV Cross-Attention Fusion Network for Robust Radar-Camera Object Detection
  Bhaskar S V., Roja Reddy B. · Journal of Trends in Comput... · Jul 14, 2026

  Object detection under adverse weather remains a major challenge for autonomous driving systems. Cameras do not work well during rain, fog, and darkness, while mmWave radar works accurately in all of these conditions, regardless of illumina…

• Dual-Domain Adaptive Input Perturbation Sensitivity for Adversarial Example Detection
  Li Yue, He Gao, Hao Wang, Ming Yang et al. · Sensors · Jul 14, 2026

  Vision-sensor-based intelligent perception systems are increasingly used in safety-critical scenarios such as autonomous driving, edge surveillance, and Internet-of-Things (IoT) platforms. The vulnerability of deep neural networks to advers…

• Self-Healing Visual Recovery for Autonomous Ground Vehicles Using Camera-Only Visual Odometry
  Jakob Solberg Berntzen, Safia Fatima, Leon Moonen · arXiv · Jul 13, 2026

  Low-cost unmanned ground vehicles are often used in indoor places like warehouses, inspection corridors, and farm rows, where painted floor lines guide the robot. Line following is useful because it only needs one camera and little computin…

• Embodied Intelligence Security with Vision-language Models: A Survey
  Junxian Duan, Haisu Zhu, Canhui Liu, S Y Li et al. · Machine Intelligence Research · Jul 13, 2026

  Abstract Embodied intelligence (EI), integrating vision-language models (VLMs) with action-oriented capabilities, presents transformative potential for autonomous systems. However, deploying VLMs in safety-critical applications like self-dr…

• STRDet: Robust 3D object detection for autonomous driving via spatio-temporal feature refinement
  Li B, Lie Guo, Longxin Guan, Xu Wang et al. · Measurement Science and Tec... · Jul 13, 2026

  Abstract Camera-based 3D object detection has attracted widespread attention for autonomous driving applications. However, existing methods often lack effective feature screening mechanisms, resulting in an extremely low spatio-temporal sig…

• Imitating human longitudinal driving behaviour for customised autonomous vehicle control
  Jack Gregory, Mohammad Rokonuzzaman, Navid Mohajer · Proceedings of the Institut... · Jul 12, 2026

  The longitudinal controller of Autonomous Vehicles (AV) plays a key role in maintaining human comfort, road safety and compliance with road regulations. This study presents a novel data-driven framework for the customisation of longitudinal…

• Advanced Radar Signal Processing Using Deep Learning for Real-Time Object Detection and Tracking in Autonomous Vehicles
  Kuruba Theja Kuruba Theja, Dharavath Sunil Dharavath Sunil, Dr B Ramprasad Dr B Ramprasad · International Journal of Sc... · Jul 11, 2026

  Radar signal processing has become a fundamental technology for autonomous vehicles because of its ability to provide reliable object detection and tracking under diverse environmental conditions, including rain, fog, snow, and low-light sc…

• Path Planning Algorithm for Self-Driving Cars Based on High-Precision Maps and Improved A*
  Siyu Wang, Rong Zhou, Tian Shi, Zhen Xu et al. · SAE technical papers on CD-... · Jul 10, 2026

  <div class="section abstract"> <div class="htmlview paragraph">To address the limitations of the traditional A* algorithm in lane-level navigation, we propose an autonomous vehicle path planning algorithm based on high-precision…

• Swapping Faces, Saving Features: A Dual-Purpose Pipeline for Pedestrian Privacy in ITS
  Roba H. Farouk, Catherine M. Elias · arXiv · Jul 9, 2026

  Large-scale and diverse datasets are needed to train AI models to take real-time decisions for autonomous vehicles (AVs), an intelligent transportation system (ITS) application. Pedestrian intention and trajectory prediction are critical mo…

• On Exploring Input Resolution Scaling For Anytime LiDAR Object Detection
  Ahmet Soyyigit, Shuochao Yao, Heechul Yun · arXiv · Jul 9, 2026

  Making tradeoffs between execution latency and result utility (i.e., anytime computing) for adapting to dynamic operational requirements has been shown to enhance the performance of cyber-physical systems. In this work, we focus on enabling…

• INTENT: An LSTM Framework for Vehicle Intention Prediction in Intersection Scenarios with Comprehensive Ablation Analysis
  Logine M. Zaki, Catherine M. Elias · arXiv · Jul 9, 2026

  Vehicle intention prediction is a pivotal aspect in the agility and safety of autonomous vehicles in all driving scenarios; if genuine enhancement of autonomous vehicles are required, we need to make them adopt human interpretation of drive…