Towards Multi-Object Detection and Tracking in Urban Scenario under Uncertainties
This addresses the need for reliable perception in autonomous vehicles, though it appears incremental by combining existing methods.
The paper tackles the problem of multi-object detection and tracking for autonomous vehicles in urban scenarios under uncertainties, achieving promising tracking performance as shown by evaluation with real-world 3D LIDAR data.
Urban-oriented autonomous vehicles require a reliable perception technology to tackle the high amount of uncertainties. The recently introduced compact 3D LIDAR sensor offers a surround spatial information that can be exploited to enhance the vehicle perception. We present a real-time integrated framework of multi-target object detection and tracking using 3D LIDAR geared toward urban use. Our approach combines sensor occlusion-aware detection method with computationally efficient heuristics rule-based filtering and adaptive probabilistic tracking to handle uncertainties arising from sensing limitation of 3D LIDAR and complexity of the target object movement. The evaluation results using real-world pre-recorded 3D LIDAR data and comparison with state-of-the-art works shows that our framework is capable of achieving promising tracking performance in the urban situation.