To achieve the objectives of the VeVuSafety project, in WP1 we proposed an uncertainty-aware approach to map continuous driving space from a bird’s-eye view (BEV) for road surface segmentation and vehicle detection. Open-source point cloud data captured from real-world and simulated driving scenarios was leveraged to train an evidential deep learning model to quantify the uncertainty of segmentation and detection results, providing reliable confidence scores for the predicted outcomes. It is worth mentioning that, compared to using image-based data, point cloud data contains less sensitive information, such as license plates and faces, which is beneficial for data privacy protection. In WP2, we proposed deep generative models, such as Conditional Variational Auto-Encoders and Mixture Models with Transformer attention mechanisms, to predict heterogeneous road users’ multimodal behaviors, including moving speed, turning angle, and mutual interactions among road users. The Mixture Models, i.e. Gaussian/Laplacian Mixture Models, enable us to estimate the likelihood of different behavior patterns of road users. Our models achieved state-of-the-art performance on multiple popular benchmarks for pedestrian, vehicle, and other road users' trajectory prediction, including the ETH/UCY, nuScenes, and Argoverse benchmarks. In WP3, to model road users' dynamic behaviors and their interactions, we proposed Graph Convolutional Networks to learn the constraints of environmental contexts, such as road lanes from high-definition maps, and the guidance of predecessors by tracing the trajectories of road users who demonstrate similar movement dynamics in the same scene. Our method outperformed concurrent approaches and won the Best Paper Award at the ROAD++ workshop at ICCV 2023. In WP4, to facilitate autonomous driving and enhance traffic safety, we proposed incorporating physical models, such as social force models with different forces (e.g. repulsive forces for collision avoidance and attractive forces for social connections), to reduce potential collisions that deep learning models may generate. The research results have been published in top-tier journals and international conference proceedings with open access and regularly promoted on the VeVuSafety project website and LinkedIn, in order to facilitate further research in the autonomous driving community. Furthermore, to reach a broader audience, especially graduate and PhD students, we organized workshops at IEEE ITSC and IV, as well as seminars at multiple universities to present the research of the VeVuSafety project.
