SafeCritic: Collision-Aware Trajectory Prediction
This addresses safety-critical trajectory prediction for autonomous systems, focusing on pedestrians and cyclists, with incremental advancements in integrating safety constraints.
The paper tackles the problem of predicting safe trajectories for vulnerable road users in urban environments by proposing SafeCritic, which combines generative adversarial networks and reinforcement learning to generate realistic and collision-aware trajectories, achieving considerable improvement over state-of-the-art on two large-scale datasets.
Navigating complex urban environments safely is a key to realize fully autonomous systems. Predicting future locations of vulnerable road users, such as pedestrians and cyclists, thus, has received a lot of attention in the recent years. While previous works have addressed modeling interactions with the static (obstacles) and dynamic (humans) environment agents, we address an important gap in trajectory prediction. We propose SafeCritic, a model that synergizes generative adversarial networks for generating multiple "real" trajectories with reinforcement learning to generate "safe" trajectories. The Discriminator evaluates the generated candidates on whether they are consistent with the observed inputs. The Critic network is environmentally aware to prune trajectories that are in collision or are in violation with the environment. The auto-encoding loss stabilizes training and prevents mode-collapse. We demonstrate results on two large scale data sets with a considerable improvement over state-of-the-art. We also show that the Critic is able to classify the safety of trajectories.