Low Latency And Low-Level Sensor Fusion For Automotive Use-Cases

Abstract

This work proposes a probabilistic low level automotive sensor fusion approach using LiDAR, RADAR and camera data. The method is stateless and directly operates on associated data from all sensor modalities. Tracking is not used, in order to reduce the object detection latency and create existence hypotheses per frame. The probabilistic fusion uses input from 3D and 2D space. An association method using a combination of overlap and distance metrics, avoiding the need for sensor synchronization is proposed. A Bayesian network executes the sensor fusion. The proposed approach is compared with a state of the art fusion system, which is using multiple sensors of the same modality and relies on tracking for object detection. Evaluation was done using low level sensor data recorded in an urban environment. The test results show that the low level sensor fusion reduces the object detection latency.