Thesis - Machine Learning for the virtual testing of autonomous driving vehicles
Automotive companies are intensively developing automated driving functions and vehicles. Unfortunately, we have already seen severe road accidents involving automated vehicles. Thus, the safety and reliability of autonomous driving vehicles (ADV) need to be tested and verified under different environmental and traffic conditions.
But, how to test ADV in a safe way for all critical situations?
A naïve approach would be virtual testing of all possible parameter and scenario combinations that influence the ADV sense, plan, and action system. Unfortunately, such testing with a full factorial variation of the parameters is not feasible.
In AVL, we are developing methods for the efficient identification of critical scenarios.
• Performance comparison of the Machine Learning methods developed at AVL
• Identification of the limitations of the Machine Learning methods developed at AVL
• Analysing the methods by using cognitive testing methods available in the literature on a publicly available AD stack like Autowave and Apollo
===
• Ongoing studies in the fields of Computer Science, Telematics, Physics or Electrical Engineering
• Good programming skills in Python or C++
• Knowledge of Machine Learning
• Good knowledge of German and English
• Knowledge of optimization methods is beneficial
• For this thesis is your presence at our headquarter in Graz required!
===
AVL is one of the world’s leading mobility technology companies for development, simulation and testing in the automotive industry, and beyond. The company provides concepts, solutions and methodologies in fields like vehicle development and integration, e-mobility, automated and connected mobility (ADAS/AD), and software for a greener, safer, better world of mobility.
But, how to test ADV in a safe way for all critical situations?
A naïve approach would be virtual testing of all possible parameter and scenario combinations that influence the ADV sense, plan, and action system. Unfortunately, such testing with a full factorial variation of the parameters is not feasible.
In AVL, we are developing methods for the efficient identification of critical scenarios.
• Performance comparison of the Machine Learning methods developed at AVL
• Identification of the limitations of the Machine Learning methods developed at AVL
• Analysing the methods by using cognitive testing methods available in the literature on a publicly available AD stack like Autowave and Apollo
===
• Ongoing studies in the fields of Computer Science, Telematics, Physics or Electrical Engineering
• Good programming skills in Python or C++
• Knowledge of Machine Learning
• Good knowledge of German and English
• Knowledge of optimization methods is beneficial
• For this thesis is your presence at our headquarter in Graz required!
===
AVL is one of the world’s leading mobility technology companies for development, simulation and testing in the automotive industry, and beyond. The company provides concepts, solutions and methodologies in fields like vehicle development and integration, e-mobility, automated and connected mobility (ADAS/AD), and software for a greener, safer, better world of mobility.
Bleibe auf dem Laufenden und verpasse kein Stellenangebot mehr!
Erhalte alle neuen Stellenanzeigen für: Environmental Engineer in Graz
E-Mails können jederzeit abbestellt werden.