TruPercept: Trust Modelling for Autonomous Vehicle Cooperative Perception from Synthetic Data
This addresses the challenge of robust perception for autonomous vehicles through inter-vehicle communication, but it is incremental as it builds on existing cooperative perception methods by adding trust modeling.
The paper tackles the problem of unreliable communication in autonomous vehicle cooperative perception by proposing a trust modeling approach that fuses peer-reported object detections based on local verification, resulting in improved perception performance beyond line of sight and at greater distances. It also introduces a synthetic dataset and framework for testing, including scenarios with unreliable and malicious behavior.
Inter-vehicle communication for autonomous vehicles (AVs) stands to provide significant benefits in terms of perception robustness. We propose a novel approach for AVs to communicate perceptual observations, tempered by trust modelling of peers providing reports. Based on the accuracy of reported object detections as verified locally, communicated messages can be fused to augment perception performance beyond line of sight and at great distance from the ego vehicle. Also presented is a new synthetic dataset which can be used to test cooperative perception. The TruPercept dataset includes unreliable and malicious behaviour scenarios to experiment with some challenges cooperative perception introduces. The TruPercept runtime and evaluation framework allows modular component replacement to facilitate ablation studies as well as the creation of new trust scenarios we are able to show.