Multi-Path Prediction of Mixed Traffic Trajectories in Shared Spaces

In shared spaces, road signs, signals, and markings are removed to allow mixed traffic directly interact with each other. The traffic engineer Reid defined it as a street encouraging pedestrian movement and reducing the dominance of vehicles without explicit traffic rules. All users have to follow informal social protocols and negotiation to use the road resources, and avoid any potential collisions. The lack of regulations makes interactions between multimodal road users more complex compared with conventional designs. With the availability of large scale datasets and the development of deep learning techniques in sequence modeling and prediction, deep learning approaches are widely used for trajectory prediction.

However, most of the approaches in trajectory modeling output a single deterministic prediction. Surrounded by a dynamic environment (other moving road users), a road user normally have more than one feasible choices under the circumstance he or she encounters. For example, a pedestrian can either choose to pass a stopped vehicle before or behind it. Instead of generating a single deterministic trajectory, generative models output multiple feasible trajectories. The most successful approach is Conditional Variational Auto-Encoder (CAVE). To this end, this master thesis aims at learning CAVE for multi-path prediction of mixed traffic trajectories in shared spaces.