Utilizing Hybrid Trajectory Prediction Models to Recognize Highly Interactive Traffic Scenarios

TL;DR

This paper presents a hybrid neural network approach combining CNN and GNN for trajectory prediction to identify highly interactive traffic scenarios, enhancing autonomous vehicle safety validation.

Contribution

It introduces a novel imitation learning model that integrates spatial and relational data to detect critical traffic situations for testing autonomous driving systems.

Findings

The model effectively predicts trajectories in complex traffic scenes.

Interactivity measures correlate with scene importance for validation.

Using social component activity improves scenario detection.

Abstract

Autonomous vehicles hold great promise in improving the future of transportation. The driving models used in these vehicles are based on neural networks, which can be difficult to validate. However, ensuring the safety of these models is crucial. Traditional field tests can be costly, time-consuming, and dangerous. To address these issues, scenario-based closed-loop simulations can simulate many hours of vehicle operation in a shorter amount of time and allow for specific investigation of important situations. Nonetheless, the detection of relevant traffic scenarios that also offer substantial testing benefits remains a significant challenge. To address this need, in this paper we build an imitation learning based trajectory prediction for traffic participants. We combine an image-based (CNN) approach to represent spatial environmental factors and a graph-based (GNN) approach to specifically represent relations between traffic participants. In our understanding, traffic scenes that are highly interactive due to the network's significant utilization of the social component are more pertinent for a validation process. Therefore, we propose to use the activity of such sub networks as a measure of interactivity of a traffic scene. We evaluate our model using a motion dataset and discuss the value of the relationship information with respect to different traffic situations.