Towards Safe Autonomous Driving
Doktorsavhandling, 2021

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.




neural networks

safety benefit

Autonomous driving

predictive evaluation


Arian Ranjbar

Chalmers, Elektroteknik, System- och reglerteknik

Integrated bicyclist protection systems - potential of head injury reduction combining passive and active protection systems

24th International Technical Conference on the Enhanced Safety of Vehicles,; (2015)

Paper i proceeding

Predicted road traffic fatalities in Germany: The potential and limitations of vehicle safety technologies from passive safety to highly automated driving

Conference proceedings International Research Council on the Biomechanics of Injury, IRCOBI,; Vol. 2018-September(2018)p. 17-52

Paper i proceeding

Driving scene retrieval by example from large-scale data

CVPR Workshops 2019,; (2019)

Paper i proceeding

Scene Novelty Prediction from Unsupervised Discriminative Feature Learning

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC),; (2020)

Paper i proceeding

Arian Ranjbar, Sascha Hornauer, Jonas Fredriksson, Stella X. Yu, Ching-Yao Chan. Safety Monitoring of Neural Networks Using Unsupervised Feature Learning and Novelty Estimation

Arian Ranjbar, Nils Lubbe, Erik Rosen, Jonas Fredriksson. Car-to-pedestrian forward collision warning revisited: A safety benefit estimation

Road traffic accidents are one of the ten largest global health problems, killing more than one million people every year. During the last decades significant efforts have been put into developing safety systems to mitigate and reduce the number of accidents. The first systems developed were passive systems, with the aim of reducing injuries when a collision is happening, such as seat belts and airbags. Later on, active safety systems entered the market, with the purpose of preventing a crash or reducing the severity of the outcome, before the crash happened. Among these systems are the autonomous emergency braking which automatically brakes when a collision is predicted. The next step in terms of safety might be autonomous driving, letting the car not only brake by itself, but drive itself. A common component in developing autonomous vehicles is machine learning, essentially training the car to perform different tasks.

This thesis aim to look into the safety potential of autonomous driving, presenting methods of how the effectiveness and safety benefit can be predicted before the vehicles have entered the market. However, these methods only account for scenarios and accidents occurring in traffic today. To prevent the system from causing new types of accidents, verification methods are implemented, with the aim of assuring safe operation. This is particularly challenging for components using machine learning, due to the complexity and the vast amount of data required to cover all potential scenarios. The second part of the thesis aim to contribute towards the safety by using monitoring systems, comparing the driving environment to what the algorithms have previously seen in the training. Predictions made in novel environments are then considered to be less reliable. This in turn can be transmitted to the decision making of the vehicle, in order to perform conservative driving actions when provided with unreliable information, hopefully preventing potentially dangerous situations.


Robotteknik och automation



Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5070



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