Politecnico di Torino (logo)

Trajectory Planning for Self-Driving Cars

Claudia Viglietti

Trajectory Planning for Self-Driving Cars.

Rel. Stefano Alberto Malan. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2022

PDF (Tesi_di_laurea) - Tesi
Licenza: Creative Commons Attribution Non-commercial No Derivatives.

Download (8MB) | Preview

This thesis aims to achieve a better understanding about trajectory and path planning in self-driving. The author participated to the Bosch Future Mobility Challenge 2022 (BFMC 2022), a challenge proposed by Bosch Romania in which students are asked to develop autonomous driving and connectivity algorithms on 1 : 10 scaled vehicles. In the competition, the car must perform specific tasks: lane follow, lane keeping, intersection detection, traffic sign and traffic light recognition, parking manoeuvre, overtake manoeuvre, object detection, trajectory and path planning based on graphs and GPS connection. At the beginning, all members of the team worked together to make the vehicle able to fulfill the first 6 tasks. Next, each one had its own assignment and the author job was studying the localisation system and trajectory planning; path planning was studied additionally after the competition to complete a methodological study. The objective of this work is to find a predefined path where the car is able to perform all the tasks required by the challenge and to go deeper into comprehend how to find the shortest path knowing the starting and final points.

Relators: Stefano Alberto Malan
Academic year: 2022/23
Publication type: Electronic
Number of Pages: 75
Corso di laurea: Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica)
Classe di laurea: New organization > Master science > LM-25 - AUTOMATION ENGINEERING
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/24539
Modify record (reserved for operators) Modify record (reserved for operators)