Efficient Perception in Automotive Detection and Tracking Using Neuromorphic Computing
2026-07-06 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial Intelligence
AI summaryⓘ
The authors studied a brain-inspired type of computer called Spiking Neural Networks (SNNs), which use less energy than usual deep learning methods. They tested SNNs on tasks that help self-driving cars see and follow multiple objects, like other cars or pedestrians. Using a special method called transfer learning, their SNN system performed well on popular driving datasets, almost matching conventional approaches. This shows that SNNs could be a good, energy-saving option for real-life autonomous vehicles.
Spiking Neural NetworksNeuromorphic ComputingTransfer LearningObject DetectionObject TrackingAutonomous VehiclesKITTI DatasetBDD100K DatasetMean Average PrecisionHigher Order Tracking Accuracy
Authors
Manish Kolachalam, Rani Malhotra
Abstract
Deep learning algorithms are notorious for their high carbon footprint and computational demands that limit their deployment on edge devices and raise concerns about their long-term sustainability. Neuromorphic computing and Spiking Neural Networks (SNNs) offer a promising alternative to traditional Von Neumann architectures, providing energy-efficient performance, massively parallel computation, and on-chip learning capabilities. Autonomous machines represent a critical application domain where these advantages are particularly valuable. We present the first comprehensive evaluation of SNNs for real-world automotive multi-object detection and tracking. Using transfer learning with the SpikeYOLO architecture, we achieve mean Average Precision of 0.937 on the KITTI dataset and 0.771 on BDD100K MOT2020 dataset for object detection and a Higher Order Tracking Accuracy score of 0.701 (KITTI) and 0.445 (BDD100K MOT2020) for object tracking--results competitive with conventional deep learning methods. Our results demonstrate that SNNs can deliver high-performance object detection and tracking in an energy efficient manner, establishing their viability for perception in real-world autonomous systems.