Trajectory Planning for Autonomous Trucks Respecting Dynamic Constraints

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Supervisor:
Kiss Domokos
Department of Automation and Applied Informatics

Autonomous driving is one of the most popular fields of research today in both the automotive industry and the

academic world. The main focus of this thesis is motion planning, a relatively small, but still huge

part of it (besides sensing and perception of the environment, situation analysis, decision,

controlling, etc.).

The purpose of this thesis is to study motion planning algortihms, and propose feasible variants

for use in embedded systems, considering the limitations of the target platform. Since

general motion planning is incredibly complex, the scope is limited to highway driving.

After a short overview of the literature and theoretical background, A MATLAB/Simulink simulation

framework is introduced, which is capable of testing different planning algorithms, provides the

possibility to insert and move obstacles in their environment, and select the vehicle models used

by the planners. Two algorithms are compared, the Dynamic Window Approach (DWA), and the Rapidly

Exploring Random Trees (RRT), both of which have been tailored for the simulation environment, commercial

vehicles, and highway driving. It is shown, that the added benefits of the more complex planner

(RRT) are not always significant (if there are any), but can make some scenarios solvable, which

is unsolvable by the simpler planner (DWA).

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