2019/10/15 by Tessa van der Heiden, van der Heiden, Tessa, Naveen Shankar Nagaraja +5 · 19 citations
Computer Science · Engineering · Mathematics · #Adversarial Robustness in Machine Learning #Adversarial system #Anomaly Detection Techniques and Applications #Artificial intelligence #Autonomous Vehicle Technology and Safety #Collision #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer security #Discriminator #Distributed computing #Encoding (memory) #FOS: Computer and information sciences #Generative grammar #Human–computer interaction #Key (lock) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine learning #Mode (computer interface) #Real-time computing #Reinforcement learning #Scale (ratio) #Training (meteorology) #Trajectory #cs.CV #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1910.06673
published in arXiv (Cornell University) (Cornell University) · To Appear as workshop paper for the British Machine Vision Conference (BMVC) 2019
arxiv created 2019/10/15 · openalex publication_date 2019/10/15 · arxiv updated 2019/10/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
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.