Analysis and Study of Self-driving bikes
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Mobility engineering (MPMOB), MSc
Publicerad
2024
Författare
Kuduva Prakash, Brijesh
Jayachandran, Harish
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Abstract
Advancements in vehicle technologies and their active safety systems have necessitated various testing methods to ensure safety and reliability. Traditional methods using stationary bikes or simple mobile platforms lack realistic behaviour as compared to a bicyclist. Testing for bikes is crucial as cycling is a major mode of transportation for the general population. The increase in bicycles [1] and e-scooter usage [2], prompts Original equipment manufacturers, to decrease and mitigate the safety risks for these users termed as vulnerable road users. The Self-driving bike project aims to bridge this gap by developing bikes and e-scooter that mimic human riding behavior. These create realistic test scenarios to evaluate vehicle safety systems which is supported by a collaboration with Volvo Cars. This thesis contributes to the development of self-driving bikes and e-scooters by focusing on areas, including the remodelling of the steering motor mount for the e-scooter for improved durability, addressing cable management issues, and designing a 3D-printed roller for indoor testing of the e-scooter. Fine-tuning the steering motor through ESCON Studio and calibrating the motor contributed to optimize the steering angle range. The configuration and setting up of self-driving bikes and e-scooter for field tests at Astazero, collecting and analyzing data to refine performance along with an analysis of indoor and outdoor tests, focusing on balance, steering rates, and other performance metrics under various conditions. These contributions enhance the testing mechanisms for self-driving bikes and e-scooter, bridging the gap between stationary methods and real-world scenarios.
Beskrivning
Ämne/nyckelord
Keywords: Simulations, Analysis, Self-driving bikes, E-scooters, Testing.