mlsanityData to 14 Oct 2026

V2X-Lead: LiDAR-based End-to-End Autonomous Driving with Vehicle-to-Everything Communication Integration

arXiv 2309.15252 ↗First posted 26 Sep 2023cs.RO cs.LGcited by 2

On the map: End-to-end autonomous driving → Cooperative and V2X driving

Abstract (arXiv)

This paper presents a LiDAR-based end-to-end autonomous driving method with Vehicle-to-Everything (V2X) communication integration, termed V2X-Lead, to address the challenges of navigating unregulated urban scenarios under mixed-autonomy traffic conditions. The proposed method aims to handle imperfect partial observations by fusing the onboard LiDAR sensor and V2X communication data. A model-free and off-policy deep reinforcement learning (DRL) algorithm is employed to train the driving agent, which incorporates a carefully designed reward function and multi-task learning technique to enhance generalization across diverse driving tasks and scenarios. Experimental results demonstrate the effectiveness of the proposed approach in improving safety and efficiency in the task of traversing unsignalized intersections in mixed-autonomy traffic, and its generalizability to previously unseen scenarios, such as roundabouts. The integration of V2X communication offers a significant data source for autonomous vehicles (AVs) to perceive their surroundings beyond onboard sensors, resulting in a more accurate and comprehensive perception of the driving environment and more safe and robust driving behavior.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving. Builds on 18 of its 34 references that are in the map. First posted on arXiv 26 Sep 2023.

Builds on

18 of 34 references.

References · 18

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  3. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 721
  4. 2022V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformercited by 320
  5. 2021MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learningcited by 290
  6. 2020PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentationcited by 275
  7. 2020A Survey of End-to-End Driving: Architectures and Training Methodscited by 123
  8. 2020MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Mapscited by 100
  9. 2018CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-drivingcited by 97
  10. 2018Motion Planning Networkscited by 80
  11. 2017Flow: A Modular Learning Framework for Mixed Autonomy Trafficcited by 73
  12. 2021Efficient and Robust LiDAR-Based End-to-End Navigationcited by 22
6 more
  1. 2022State Dropout-Based Curriculum Reinforcement Learning for Self-Driving at Unsignalized Intersectionscited by 16
  2. 2022Unified Automatic Control of Vehicular Systems with Reinforcement Learningcited by 13
  3. 2020End-to-end Autonomous Driving Perception with Sequential Latent Representation Learningcited by 11
  4. 2021Carl-Lead: Lidar-based End-to-End Autonomous Driving with Contrastive Deep Reinforcement Learningcited by 11
  5. 2022AVstack: An Open-Source, Reconfigurable Platform for Autonomous Vehicle Developmentcited by 6
  6. 2021Trust but Verify: Cryptographic Data Privacy for Mobility Managementcited by 2

Built on by

Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutionscited by 7
  2. 2025Risk Map As Middleware: Towards Interpretable Cooperative End-to-end Autonomous Driving for Risk-Aware Planningcited by 5

Similar, not cited

Close in wording, no citation either way

  1. 2025Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition
  2. 2024End-to-End Autonomous Driving through V2X Cooperation
  3. 2024Towards Collaborative Autonomous Driving: Simulation Platform and End-to-End System
  4. 2024Unified End-to-End V2X Cooperative Autonomous Driving
  5. 2022Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersections
  6. 2025A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator
  7. 2023End-to-end Autonomous Driving using Deep Learning: A Systematic Review
  8. 2024V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models