LiDAR Technology for Intelligent Transportation Systems and Smart Driving
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".
Deadline for manuscript submissions: 25 February 2027 | Viewed by 28
Special Issue Editors
Interests: LiDAR; sensing technology; driving behavior analysis; intelligent transportation; data-driven traffic safety analysis
Special Issues, Collections and Topics in MDPI journals
Interests: computer vision; deep learning; structural health monitoring; intelligent transportation systems; Bayesian statistics; cyber–physical systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Light Detection and Ranging (LiDAR) has rapidly evolved from a specialized surveying instrument into a foundational sensing technology for intelligent transportation systems (ITSs) and smart driving. Over the past two decades, both roadside (infrastructure-based) LiDAR and mobile LiDAR have demonstrated the ability to deliver high-resolution, three-dimensional, all-light-condition perception of vehicles, pedestrians, cyclists, animals, obstacle objects on roads, and roadway infrastructure. These capabilities are increasingly central to traffic safety analysis, connected and autonomous vehicle (CAV) operations, vulnerable road user protection, work zone monitoring, asset management, and digital twin construction for transportation networks. At the same time, the field is being transformed by the convergence of LiDAR sensing and classical machine learning, modern deep learning, large multi-modal AI models, and geospatial intelligence, opening new opportunities for accurate, real-time, and explainable transportation analytics.
In parallel with these technical advances, there is a clear and growing demand from practice. State and local Departments of Transportation, consulting firms, and CAV developers are deploying LiDAR-based systems at intersections, corridors, and on instrumented vehicles to support Vision Zero programs, infrastructure inventory, lane-keeping and trajectory studies, and CAV testbeds. Federal and state funding programs, as well as industry partnerships, are accelerating the transition from prototypes to operational systems. This combination of maturing algorithms, falling sensor costs, AI integration, and a well-defined application market has created a unique window for impactful research and engineering innovation.
This Special Issue aims to present and disseminate the most recent advances in LiDAR sensing for intelligent transportation systems and smart driving, bridging algorithmic research and real-world deployment. We welcome the submission of high-quality theoretical, methodological, and applied contributions, including empirical case studies and large-scale field deployments, from academia, government agencies, and industry. Topics of interest include, but are not limited to, the following:
- Roadside (infrastructure-based) LiDAR systems for traffic detection, tracking, and trajectory extraction.
- Mobile LiDAR for road asset inventory, pavement condition assessment, and high-definition mapping.
- LiDAR-based perception for connected and autonomous vehicles, including lane-keeping and motion planning.
- Sensor fusion of LiDAR with cameras, radar, GNSS/IMU, and V2X data.
- Deep learning, foundation models, and geospatial AI for point cloud processing and scene understanding.
- Pedestrian, cyclist, and wheelchair user detection, tracking, trajectory prediction, and risk assessment.
- LiDAR data quality, denoising, and robustness under adverse weather (rain, snow, fog, dust).
- Digital twins, virtual reality, and simulation environments built from LiDAR data for ITS and CAV testing.
- Real-time edge computing, embedded systems, and low-latency architectures for LiDAR analytics.
- Engineering deployment, calibration, standardization, privacy, and cybersecurity of LiDAR-based ITSs.
Applications in work zones, rural highways, intersections, transit, and freight corridors.
Prof. Dr. Hao Xu
Dr. Shanglian Zhou
Guest Editors
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Keywords
- LiDAR
- roadside LiDAR
- mobile LiDAR
- intelligent transportation systems (ITS)
- connected and autonomous vehicles (CAVs)
- point cloud processing
- deep learning
- sensor fusion
- traffic safety
- pedestrian detection
- trajectory prediction
- digital twins
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