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A Distributed Time-of-Flight Sensor System for Autonomous Vehicles: Architecture, Sensor Fusion, and Spiking Neural Network Perception

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Veröffentlicht in:Electronics vol. 14, no. 7 (2025), p. 1375
1. Verfasser: Lielamurs, Edgars
Weitere Verfasser: Ibrahim, Sayed, Cvetkovs, Andrejs, Novickis, Rihards, Zencovs, Anatolijs, Celitans, Maksis, Bizuns, Andis, Dimitrakopoulos, George, Koszescha, Jochen, Ozols, Kaspars
Veröffentlicht:
MDPI AG
Schlagworte:
Data management
Test vehicles
Software
Cameras
Neural networks
Sensors
Computer vision
Communication
Lasers
Image registration
Spatial resolution
Autonomous vehicles
Network latency
Energy efficiency
Registration
Stereoscopy
Algorithms
Automation
Lidar
Light
Multisensor fusion
Robotics
Environmental awareness
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Abstract:Mechanically scanning LiDAR imaging sensors are abundantly used in applications ranging from basic safety assistance to high-level automated driving, offering excellent spatial resolution and full surround-view coverage in most scenarios. However, their complex optomechanical structure introduces limitations, namely limited mounting options and blind zones, especially in elongated vehicles. To mitigate these challenges, we propose a distributed Time-of-Flight (ToF) sensor system with a flexible hardware–software architecture designed for multi-sensor synchronous triggering and fusion. We formalize the sensor triggering, interference mitigation scheme, data aggregation and fusion procedures and highlight challenges in achieving accurate global registration with current state-of-the-art methods. The resulting surround view visual information is then applied to Spiking Neural Network (SNN)-based object detection and probabilistic occupancy grid mapping (OGM) for enhanced environmental awareness. The proposed system is demonstrated on a test vehicle, achieving coverage of blind zones in a range of 0.5–6 m with a scalable and reconfigurable sensor mounting setup. Using seven ToF sensors, we can achieve a 10 Hz synchronized frame rate, with a 360° point cloud registration and fusion latency below 40 ms. We collected real-world driving data to evaluate the system, achieving 65% mean Average Precision (mAP) in object detection with our SNN. Overall, this work presents a replacement or addition to LiDAR in future high-level automation tasks, offering improved coverage and system integration.
ISSN:2079-9292
DOI:10.3390/electronics14071375
Quelle:Advanced Technologies & Aerospace Database