Motion prediction of vehicles surrounding the GRT vehicle

Motion prediction of vehicles surrounding the GRT vehicle: Interaction-aware motion prediction model

Overgaag, Gijs (TU Delft Mechanical, Maritime and Materials Engineering)

Ruigrok, Rob (mentor)
De Schutter, B.H.K. (graduation committee)
Shyrokau, B. (graduation committee)

Delft University of Technology

Mechanical Engineering | Vehicle Engineering

2021-06-28

2getthere specialises in autonomous people transport through their GRT vehicle, used for transporting people at the airport from the parking to the terminal to ensure the GRT can operate comfortably and safely in a mixed traffic environment. The vehicle needs to plan a smooth and collision-free path. An essential aspect of a safe path is to predict the motion of the surrounding vehicles of the GRT to guarantee a smooth adaption to the constantly changing traffic scenario. The smooth adaption to the changing traffic scenario is essential for the GRT. The GRT cannot brake with the same magnitude as surrounding traffic due to standing people inside the vehicle. The importance of the smooth adaption to the traffic scenario resulted in the following problem statement; is it possible to create a motion prediction model for the surrounding vehicles of the ego vehicle to allow for pro-active velocity planning of the ego vehicle itself. From the literature, motion prediction models can be divided into physics-based motion models, manoeuvre-based motion models, and interaction-aware motion models. Each level has a certain amount of building blocks, with each level being an extra addition of situation awareness to the previous level.
An interaction-aware motion model is the most advanced prediction model taking the infrastructure and interaction between the vehicles in the traffic scene into account. Therefore this principle of motion prediction is chosen for the motion prediction of the surrounding vehicles of the GRT. First, all possible state and route information of the surrounding vehicles are gathered. Secondly, a tree is created based on the vehicles' routes in the traffic situation, with the unique combination of manoeuvres known as a single branch of the scenario tree. Hereafter, the possible conflict areas between the individual manoeuvres are determined based on the desired trajectories for a specific route. The first vehicle passing the conflict area will influence the second vehicle's trajectory for passing the same conflict area, which will influence the third vehicle's trajectory and so on. Each vehicle can avoid a collision by passing in front or behind the previous vehicle(s) at the conflict area. Evaluating all passing possibilities will result in the velocity trajectory with the lowest cost, constrained by comfort. The sum of all costs for each branch of manoeuvres is used as a metric to determine the possible velocity profiles for each vehicle in the current traffic situation, resulting in a better understanding of the traffic scenario and ensuring a comfortable adaption to a changing traffic scenario. This thesis will evaluate several methods found in the literature to built a motion prediction model suitable for the application at 2getthere. The chosen motion model will be evaluated by two scenarios in a T-junction intersection.

Motion prediction
Interaction-aware
Trajectory planning

http://resolver.tudelft.nl/uuid:a65e3e79-9d5a-4512-8113-37612f9817d9

Student theses

master thesis

© 2021 Gijs Overgaag