Trajectory Forecasting in Autonomous Vehicles

This lecture by the instructor covers the topic of trajectory forecasting in autonomous vehicles, focusing on deep learning models for predicting human trajectories in socially-aware transportation scenarios. The lecture delves into the challenges of sequence modelling and encoding social interactions, presenting the TrajNet++ benchmark for evaluating forecasting models. It discusses the types of trajectories, the importance of understanding complex scenarios, and the different categories of human interactions in pedestrian forecasting. The lecture also explores grid-based and non-grid-based interaction modules, attention mechanisms, and the use of transformer networks in trajectory forecasting.

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