Adv-BMT: Bidirectional Motion Transformer for Safety-Critical Traffic Scenario Generation

TL;DR

Adv-BMT introduces a bidirectional motion transformer to generate diverse, realistic, and safety-critical traffic scenarios for autonomous driving testing, significantly enhancing dataset diversity without collision data pretraining.

Contribution

The paper presents a novel bidirectional motion transformer framework that generates realistic adversarial traffic scenarios without collision data pretraining, improving safety testing for autonomous vehicles.

Findings

Reduces collision rates by 20% in augmented datasets

Generates diverse and realistic traffic interactions

Does not require collision data for training

Abstract

Scenario-based testing is essential for validating the performance of autonomous driving (AD) systems. However, such testing is limited by the scarcity of long-tailed, safety-critical scenarios in existing datasets collected in the real world. To tackle the data issue, we propose the Adv-BMT framework, which augments real-world scenarios with diverse and realistic adversarial traffic interactions. The core component of Adv-BMT is a bidirectional motion transformer (BMT) model to perform inverse traffic motion predictions, which takes agent information in the last time step of the scenario as input, and reconstructs the traffic in the inverse of chronological order until the initial time step. The Adv-BMT framework is a two-staged pipeline: it first conducts adversarial initializations and then inverse motion predictions. Different from previous work, we do not need any collision data for pretraining, and are able to generate realistic and diverse collision interactions. Our experimental results validate the quality of generated collision scenarios by Adv-BMT: training in our augmented dataset would reduce episode collision rates by 20%. Demo and code are available at: https://metadriverse.github.io/adv-bmt/.