End-to-End and Safety-Certifiable Autonomous Vehicles for Logistics Applications

Abstract

Autonomous vehicles hold transformative potential for logistics and urban mobility, yet deploying them safely in real-world environments remains a grand challenge. This research focuses on developing end-to-end learning frameworks and safety-certifiable navigation systems for autonomous vehicles in logistics applications — from campus delivery and last-mile transportation to urban freight operations.

Our approach integrates three core elements:

End-to-End Autonomous Driving — We develop neural network architectures that learn to drive directly from raw sensor inputs (LiDAR, camera, IMU, GNSS) to control outputs, enabling autonomous vehicles to handle complex urban scenarios including dense traffic, dynamic obstacles, and GPS-degraded environments. Our end-to-end pipelines unify perception, prediction, planning, and control into a single differentiable framework.
Safety Certification and Integrity Monitoring — Unlike conventional black-box approaches, our systems incorporate rigorous safety certification mechanisms. We design integrity monitoring algorithms that quantify the trustworthiness of navigation solutions in real time, enabling the vehicle to detect unsafe states and trigger fail-safe maneuvers. This is critical for logistics applications where reliability and regulatory compliance are paramount.
Real-World Deployment for Logistics — We bridge the gap between research and application by developing full-stack autonomous vehicle platforms for logistics use cases, including campus patrol, autonomous delivery, and connected fleet management. Our platforms feature multi-sensor fusion (GNSS-RTK/LiDAR/Camera/IMU), V2X communication, and robust localization in challenging urban canyon environments.

End-to-End and Safety-Certifiable Autonomous Vehicles for Logistics Applications

Demo Video

Autonomous Driving Test — TAS Lab, PolyU

Selected Publications (*: Corresponding author)

Integrated Planning and Control on Manifolds: Factor Graph Representation and Toolkit.
Yang, P., Wen, W., Yang, R., Zhang, Y., Hu, J., Chen, Y., Xiao, N., Zhao, J.
IEEE International Conference on Robotics & Automation (ICRA), 2026.
EIRM-RL: Epistemic Integrity Risk Monitoring Inspired Safe Reinforcement Learning for Trustworthy Autonomous Navigation.
Zhang, Y., Wang, Y., Wen, W.
IEEE Internet of Things Journal, 13(2), 3500-3512, 2025. (IF: 8.9, JCR Q1)
Learning Safe, Optimal, Real-Time Flight Interaction with Deep Confidence-enhanced Reachability Guarantee.
Zhang, Y., Wang, Y., Yan, P., Wen, W.
IEEE Transactions on Intelligent Transportation Systems, 2025. (IF: 8.4, JCR Q1)
Safety-quantifiable Line Feature-based Monocular Visual Localization with 3D Prior Map.
Zheng, X., Wen, W.*, Hsu, L.T.
IEEE Transactions on Intelligent Transportation Systems, 2025. (IF: 8.4, JCR Q1, Citations: 3)
Continuous Error Map Aided Adaptive Multi-Sensor Integration for Connected Autonomous Vehicles in Urban Scenarios.
Huang, F., Wen, W.*, Zhang, G., Su, D., Huang, Y.
IEEE Transactions on Instrumentation and Measurement, 2025. (IF: 5.9, JCR Q1, Citations: 4)
Fault Detection Algorithm for Gaussian Mixture Noises: An Application in Lidar/IMU Integrated Localization Systems.
Yan, P., Li, Z., Huang, F., Wen, W., Hsu, L.T.
NAVIGATION: Journal of the Institute of Navigation, 72(1), 2025. (IF: 3.1, JCR Q1, Citations: 6)
Safety-Quantifiable Planar-Feature-based LiDAR Localization with a Prior Map for Intelligent Vehicles in Urban Scenarios.
Zhang, J., Liu, X., Wen, W.*, Hsu, L.T.
IEEE Transactions on Intelligent Vehicles, 2024. (IF: 14.3, JCR Q1, Citations: 2)
A Novel Consistent-Robust SINS/GNSS/NHC Integrated Navigation Method for Autonomous Vehicles Under Intermittent GNSS Outage.
Du, S., Huang, Y.*, Wen, W., Zhang, Y.
IEEE Transactions on Intelligent Vehicles, 2024. (IF: 14.3, JCR Q1, Citations: 13)
Tightly-coupled Visual/Inertial/Map Integration with Observability Analysis for Reliable Localization of Intelligent Vehicles.
Zheng, X., Wen, W.*, Hsu, L.T.
IEEE Transactions on Intelligent Vehicles, 2024. (IF: 14.3, JCR Q1, Citations: 3)
Integration of Vehicle Dynamic Model and System Identification Model for Extending the Navigation Service Under Sensor Failures.
Yan, P., Wen, W.*, Hsu, L.T.
IEEE Transactions on Intelligent Vehicles, 2023. (IF: 14.3, JCR Q1, Citations: 11)
Dynamic Object-Aware LiDAR Odometry Aided by Joint Weightings Estimation in Urban Areas.
Huang, F., Wen, W., Zhang, J.*, Wang, C., Hsu, L.T.
IEEE Transactions on Intelligent Vehicles, 2023. (IF: 14.3, JCR Q1, Citations: 9)
ECMD: An Event-Centric Multisensory Driving Dataset for SLAM.
Chen, P., Guan, W., Huang, F., Zhong, Y., Wen, W., Hsu, L.T., Lu, P.*
IEEE Transactions on Intelligent Vehicles, 2023. (IF: 14.3, JCR Q1, Citations: 31)
An Improved Inertial Preintegration Model in Factor Graph Optimization for High Accuracy Positioning of Intelligent Vehicles.
Zhang, L., Wen, W.*, Zhang, T., Hsu, L.T.
IEEE Transactions on Intelligent Vehicles, 2023. (IF: 14.3, JCR Q1, Citations: 16)
Safe-Assured Learning-Based Deep SE(3) Motion Joint Planning and Control for UAV Interactions with Dynamic Environments.
Zhang, Y., Wen, W., Yan, P.
IEEE ITSC 2024. (Citations: 4)
Roadside Infrastructure Assisted LiDAR/Inertial-based Mapping for Intelligent Vehicles in Urban Areas.
Huang, F., Chen, H., Urtay, A., Su, D., Wen, W., Hsu, L.T.
IEEE ITSC 2023. (Citations: 7)
Adaptive Multi-Sensor Integrated Navigation System Aided by Continuous Error Map from RSU for Autonomous Vehicles in Urban Areas.
Huang, F., Wen, W., Zhang, G., Su, D., Hsu, L.T.
IEEE ITSC 2023. (Citations: 2)
UrbanLoco: A Full Sensor Suite Dataset for Mapping and Localization in Urban Scenes.
Wen, W., Zhou, Y., Zhang, G., Fahandezh-Saadi, S., Bai, X., Zhan, W., Tomizuka, M., Hsu, L.T.
IEEE ICRA 2020, 2310-2316. (Citations: 184)
UrbanNav: An Open-sourced Multisensory Dataset for Benchmarking Positioning Algorithms Designed for Urban Areas.
Hsu, L.T., Kubo, N., Wen, W., Chen, W., Liu, Z., Suzuki, T., Meguro, J.
ION GNSS+ 2021. (Citations: 149)

→ Full publication list

Acknowledgement and Collaborators

This research is supported by government and industry partners, including the Hong Kong Polytechnic University, Guangdong Basic and Applied Basic Research Foundation, Hong Kong Smart Traffic Fund, Innovation and Technology Fund, Huawei Technologies, Meituan, Tencent, and iDriverplus. We also collaborate closely with the Mechanical Systems Control Lab at the University of California, Berkeley, and the Chemnitz University of Technology in Germany.

📁 Before 2024 — Earlier Work on Autonomous Systems Development

Autonomous vehicle platform for campus logistics and urban navigation

Key Research Contributions

End-to-end perception-to-control pipelines for autonomous driving in complex urban environments
Safety-certifiable multi-sensor fusion (GNSS-RTK/LiDAR/Camera/IMU) with real-time integrity monitoring
Factor graph optimization for robust vehicle state estimation in GPS-degraded urban canyons
V2X-assisted connected autonomous driving for fleet-level logistics coordination
Campus-scale autonomous driving demonstrations at The Hong Kong Polytechnic University

News

Sept 2022, we welcome the PolyU Campus Facilities and Sustainability Office (CFSO) and Health and Safety Office (HSO) to attend the demonstration of AAE/CFSO Campus Security Patrol with Unmanned Ground Vehicle (UGV)