Controllable Multimodal Motion Behavior Generation for Autonomous Driving

Abstract

The generation of motion behaviors plays a pivotal role in constructing effective simulated scenarios for testing autonomous driving systems (ADSs). The controllability (i.e., the ability to synthesize specific motion patterns) and multimodality (i.e., the capacity to represent multiple motion intentions) of generated motion behaviors are essential for the purposeful and comprehensive evaluation of ADS. Although recent studies have made progress in either multimodal or controllable motion behavior generation, it remains a major challenge to simultaneously generate multimodal motion behaviors in a controllable manner. In this work, we propose a unified framework, CoMoGen, to generate multimodal motion behaviors in a controllable manner under open-loop evaluation assumption. The proposed framework consists of three core components: i) a learning-based vehicle placer, responsible for positioning generated vehicles in non-conflicting initial locations; ii) a robust model-based trajectory candidate generator, capable of synthesizing controllable and multimodal trajectory candidates. iii) a learning-based trajectory selector, developed to evaluate and select multimodal trajectories for the placed vehicles. Experiments on the INTERACTION dataset demonstrate strong controllability and multimodality of CoMoGen. Further experiments on three additional real-world datasets, that are unseen during training, as well as on diverse synthesized high-definition maps, validate the remarkable generalization capability of CoMoGen.