SafePathNet: Learning a Distribution of Trajectories for Safe and Comfortable Autonomous Driving

SafePathNet introduces a novel approach that models the distribution of future trajectories for both the self-driving vehicle and other road agents using a unified neural network architecture. By incorporating a 'Mixture of Experts' framework, the model can learn diverse driving strategies and prioritize safety in real-time decision-making. The use of Transformer networks and imitation learning further enhances the model's ability to handle complex and unpredictable driving scenarios.

Read full paper: https://arxiv.org/abs/2211.02131

Tags: Autonomous Driving, AI Safety, Machine Learning