Multi-Sensor Fusion for Autonomous Vehicles and Driver Assistance Systems
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".
Deadline for manuscript submissions: 1 July 2026 | Viewed by 12
Special Issue Editor
Interests: accurate mapping systems based on optimal optimization algorithms; advanced driver assistance systems; assistive intelligent vehicles; driver and road user state and intent recognition; dynamic and cinematic car models; intelligent localization systems based on LiDAR odometry; intelligent navigation and localization systems based on inertial navigation systems; intelligent-vehicle-related image, radar, and LiDAR signal processing; sensor fusion systems for driverless cars
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Special Issue Information
Dear Colleagues,
Multi-sensor fusion is a crucial technique for safe and efficient autonomous vehicles and advanced driver assistance systems. It integrates data from various sensors, such as LiDAR, radar, cameras, GPS, and ultrasonic sensors, to create a more accurate understanding of the vehicle’s environment, location, and movement. Each sensor has its strengths and limitations; for instance, cameras provide visual information but struggle in poor lighting conditions, while radar works fine in adverse weather but lacks when it comes to detailed information. By combining data from multiple sources, sensor fusion addresses these shortcomings, improving perception, object detection, tracking, and control, and behavioural decision-making.
In autonomous vehicles, sensor fusion supports real-time situational awareness and precise localisation, essential for navigation and collision avoidance. In ADAS applications, it powers features like adaptive cruise control, lane-keeping assistance, and emergency braking. Advanced algorithms, often based on probabilistic algorithms such as Kalman filters or deep neural networks, are used to process and fuse sensor data efficiently.
This Special Issue explores novel techniques in multi-sensor fusion and AI for high-precision localisation, enhanced environmental understanding, and robust behavioural decision-making. Through the harnessing of these technologies, intelligent transportation systems can be developed, pushing the boundaries of safety, reliability, and autonomy on the road.
Dr. Iván García Daza
Guest Editor
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Keywords
- sensor fusion
- autonomous vehicles
- LiDAR
- perception
- machine learning
- localisation
- behavioural decision-making
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