SUrrogate measures for SAFE autonomous and connected mobility

Introduction

SUperSAFE "SUrrogate measures for SAFE autonomous and connected mobility" will address the problem of the safety evaluation of the interaction between conventional vehicles and connected and automated vehicles (CAVs). The project builds on the notion that vehicle automation is posing new risks that the traditional accident-based and proactive safety analysis methods are unable to investigate.

Project Objectives

In SUperSAFE, I will select the relevant variables drawn on the newly identified risks posed by CAVs. With these, I will develop a new proactive method based on surrogate measures of safety for studying the effects of the physical and digital infrastructure on the interaction between road users in a mixed-mobility environment.

Context and Importance

Also considering the benchmarks for cities’ liveability and transport sustainability that include road casualties as a primary factor, the European White Paper on Transport calls to reach zero fatalities by 2050 following Vision Zero’s policy (zero serious casualties). Recent statistics indicate a reduction of traffic accidents but also that this development has slowed and additional efforts are required.

Current Challenges

At the same time, CAVs are already a reality. The tendency towards vehicle automation is even more evident in the European policies which encourage member states to push for the introduction of vehicles with advanced driver assistance systems. However, the road towards full automation is still not open because there is a fear of crashes/injuries and low acceptance of potential CAV accidents.

Unknown Variables

This is mainly because the CAVs’ behavior vis-à-vis the conventional vehicles on the road and the digital and physical infrastructure is still unknown.

Conclusion

To meet these rapidly approaching needs, I propose SUperSAFE, which will contribute to attaining the aforementioned European goals by developing a scientifically rigorous method of estimating risk based on the road users’ real needs to improve traffic safety in the transition period to fully automated driving.