Comparative Safety Assessment of Automated Driving Strategies at Highway Merges in Mixed Traffic

Comparative Safety Assessment of Automated Driving Strategies at Highway Merges in Mixed Traffic

Mullakkal-Babu, F.A. (TU Delft Transport and Planning)
Wang, M. (TU Delft Transport and Planning)
van Arem, B. (TU Delft Transport and Planning)
Happee, R. (TU Delft Intelligent Vehicles)

2020

We present a simulation-based approach to assess the safety impacts of vehicles equipped with Automated Driving Systems (ADS) in mixed traffic with Human-driven Vehicles (HV). Specifically, we compare two generic longitudinal strategies of ADS to handle a cut-in: Reactive ADS acting only when the cut-in vehicle crosses the target lane boundary, and Predictive ADS acting at the onset of the cut-in manoeuvre. We identify their distinctive effects on the traffic safety under cut-in maneuvers of adjacent human-driven vehicles at highway merges. We employ a microscopic traffic flow simulator that describes the lane changing process with high detail, accounting for the vehicle interaction and consequent trajectory updates. These high-resolution trajectories are post-processed to estimate a set of relevant surrogate measures of safety. By analyzing these measures, we find that the predictive ADS significantly outperforms the reactive ADS in aspects such as temporal proximity to crash, expected crash severity and the driving risk (combining the two aspects), and the number of aborted lane changes by HV. The negative safety impact of reactive ADS becomes prominent at penetration rate > 10%. The major difference between the two ADS approaches appears in the dynamics of risk during the lane changing. When a vehicle cuts in ahead of Reactive ADS, the risk peaks approximately halfway through the maneuver; whereas with Predictive ADS the risk remains marginal throughout. This work demonstrates the potential of simulation-based safety assessment to differentiate the safety impacts of automation functionalities at an early stage of product development.

Traffic safety
automated driving
microscopic simulation
surrogate measure of safety
tactical decisions.

http://resolver.tudelft.nl/uuid:e35012b8-4514-46b0-969a-7d15b485916d

https://doi.org/10.1109/TITS.2020.3038866

2021-06-03

1524-9050

IEEE Transactions on Intelligent Transportation Systems, 23 (4), 3626-3639

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2020 F.A. Mullakkal-Babu, M. Wang, B. van Arem, R. Happee