Diverse and Admissible Trajectory Forecasting through Multimodal Context Understanding
This work addresses the problem of safe and reliable decision-making in autonomous driving by improving trajectory prediction for vehicles and pedestrians, though it appears incremental as it builds on existing multimodal approaches.
The paper tackles the challenge of forecasting diverse and physically admissible trajectories for multiple agents in autonomous driving by synthesizing multimodal context, achieving significant performance improvements over previous state-of-the-art methods on two public datasets.
Multi-agent trajectory forecasting in autonomous driving requires an agent to accurately anticipate the behaviors of the surrounding vehicles and pedestrians, for safe and reliable decision-making. Due to partial observability in these dynamical scenes, directly obtaining the posterior distribution over future agent trajectories remains a challenging problem. In realistic embodied environments, each agent's future trajectories should be both diverse since multiple plausible sequences of actions can be used to reach its intended goals, and admissible since they must obey physical constraints and stay in drivable areas. In this paper, we propose a model that synthesizes multiple input signals from the multimodal world|the environment's scene context and interactions between multiple surrounding agents|to best model all diverse and admissible trajectories. We compare our model with strong baselines and ablations across two public datasets and show a significant performance improvement over previous state-of-the-art methods. Lastly, we offer new metrics incorporating admissibility criteria to further study and evaluate the diversity of predictions. Codes are at: https://github.com/kami93/CMU-DATF.