The seminar can be accessed through Zoom, and it will open shortly before 13:15. We would kindly ask you to keep the video off and mute the microphone during the seminar. At the end of the session there will be an opportunity to ask questions through Zoom. In case there will be any updates about the event, these will be posted on this website.
Arian Ranjbar is a PhD student in the research group Mechatronics, Division of Systems and Control
Opponent is Professor Erik Frisk, Linköping University
Examiner is Professor Jonas Sjöberg, Division of Systems and Control
Main supervisor is Professor Jonas Fredriksson, Division of Systems and Control
Autonomous driving is expected to bring several benefits, in particular regarding safety. This thesis aim to contribute towards two questions concerning safety: “What is the potential safety benefit of autonomous driving?” and “How can we ensure safe operation of such vehicles?”. In the first part of the thesis, methods for evaluating the safety benefit are investigated. In particular predictive effectiveness evaluation based on resimulation of accident data, using models to estimate new outcomes in case the safety system had been available.
To illustrate the methodology, four examples of gradual increase in model complexity are presented. First, an Autonomous Emergency Braking (AEB) system using a sensor model, decision algorithm, vehicle dynamics model and regression based injury model. This is extended in a Forward Collision Warning (FCW) system which additionally requires a driver model to simulate driver reactions. The third example shows how an active, AEB, and passive, airbag, system can be combined. Finally the fourth example combines several systems to emulate a highly automated vehicle. Apart from predicting the real world performance, this analysis also identifies current safety gaps by studying the residual of the accident set. Safety benefit estimation using accident data gives an evaluation on the current accident distributions, however, the systems may introduce new accidents if not operated as intended.
In the second part of the thesis, safety verification processes with the intent of preventing unsafe operation, are presented. This is particularly challenging for machine learning based components, such as neural networks. In this case, traditional analytical verification approaches are difficult to apply due to the non-linearity and high dimensional parameter spaces. Similarly, statistical safety arguments often require unfeasible amounts of annotated validation data. Instead, monitor functions are investigated as a complement to increase safety during operation. The method presented estimates the similarity of the driving environment, compared to the training data, where decisions inferred from novel data can be considered less reliable. Although not providing a complete safety assurance, the methodology show promising initial results for increasing safety. In addition, it could potentially be used to collect novel data and reduce redundancy in training data.
Keywords: Autonomous driving, safety benefit, effectiveness, predictive evaluation, verification, monitoring, neural networks.