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Research of advanced strategies for Predictive Adaptive Cruise Control

Alex Amoruso

Research of advanced strategies for Predictive Adaptive Cruise Control.

Rel. Stefano Alberto Malan, Graziano Nardelli. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2021


The thesis project has been carried out in collaboration with Valeo Schalter und Sensoren GmbH located in Bietigheim-Bissingen (Baden-Württemberg, Germany) which is involved as a supplier in the automotive field for ADAS (Advanced Driver-Assistance Systems) applications. Among these, the presented thesis work will be focused on Predictive ACC (Adaptive Cruise Control) for the low-middle car segment market, i.e. using as perception components only the front camera and the information provided by SD (Standard Definition) Maps. The aim of this thesis activity is to extend the ACC features in complex scenarios such as highway exit and intersections (regulated either by stop and yield signs or by roundabout). The role in the application of the maps information is crucial in order to take decisions in advance, obtaining as much as possible smooth and smart speed adaptation actions. Firstly, the essential documentation from the ACC state of art will be reported, together with the ISO reference protocols for ADAS applications. As mentioned it is important to extract and reconstruct the information provided by the maps, so the ADASISv2 protocol will be reported, that is the reference protocol for the extraction and manipulation of the data provided by the SDMaps. The details of the entire development cycle of the required tasks have been taken care of: the thesis will therefore present the analysis of the scenarios to redact the stakeholder and system requirements documents and subsequently the main tasks carried out in the development phase will be discussed. Finally the testing methodologies will be illustrated, both in simulation and on the car, with the relative achieved results.

Relators: Stefano Alberto Malan, Graziano Nardelli
Academic year: 2021/22
Publication type: Electronic
Number of Pages: 98
Additional Information: Tesi secretata. Fulltext non presente
Corso di laurea: Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering)
Classe di laurea: New organization > Master science > LM-32 - COMPUTER SYSTEMS ENGINEERING
Aziende collaboratrici: Valeo Schalter und Sensoren GmbH
URI: http://webthesis.biblio.polito.it/id/eprint/21138
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