Predicting Safety Benefits of Automated Emergency Braking at Intersections - Virtual simulations based on real-world accident data
Doktorsavhandling, 2018

Introduction: Intersections are a global traffic safety concern. In the United States, around half of all fatal road traffic accidents take place at intersections or were related to them. In the European Union, about one fifth of road traffic fatalities occur at intersections.

Intersection Automated Emergency Braking (AEB) seems to be a promising technology with which to address intersection accidents, as information retrieval by on-board sensing is operational on its own, and, in critical situations, braking is initiated independent of driver reaction. This is not the case for Vehicle-to-Everything (V2X) communication, which requires all conflict-involved vehicles to be equipped with this technology and drivers to respond to an initiated warning. The objective of this thesis is to evaluate the effectiveness of a theoretical Intersection AEB system in avoiding accidents and mitigating injuries. As it will take several decades for a new safety technology to penetrate the vehicle fleet and full coverage of all vehicles may never be achieved, the technology benefit is here analyzed as a function of market penetration. Finally, this research assesses whether a set of test scenarios can be derived without compromising the variance of real-world accidents.

Methods: Data from the United States National Automotive Sampling System / General Estimates System and the Fatality Analysis Reporting System was used to compare the capacity of on-board sensing and V2X communication to save lives. To investigate Intersection AEB in detail, the German In-Depth Accident Study (GIDAS) data and the related Pre-Crash Matrix (PCM) were utilized to re-simulate accidents with and without Intersection AEB using different parameter settings of technical aspects and driver comfort boundaries. Machine learning techniques were used to identify opportunities for data clustering.

Result: On-board sensing has a substantially higher capability to save lives than V2X communication during the period before full market penetration of both is reached. The analysis of GIDAS and PCM data indicate that about two thirds of left-turn across path accidents with oncoming traffic (LTAP/OD) and about 80 percent of straight crossing path (SCP) accidents can be avoid by an idealized Intersection AEB. Moderate to fatal injuries could be avoided to an even higher extent. Key parameters impacting effectiveness are vehicle speed and potential path choice; to increase effectiveness, these should be limited and narrowed down, respectively.

Conclusion and Limitations: Intersection AEB is effective in reducing LTAP/OD and SCP accidents and mitigating injuries However, intersection accidents are highly diverse and accurate performance evaluation requires taking variations into account. The simulations were conducted using ideal sensing without processing delays and an ideal coefficient of friction estimation.

injury mitigation

market penetration

accident avoidance



straight crossing path



left turn across path

KC Kemigården 4
Opponent: Professor H. Clay Gabler, Ph.D., Department of Biomedical Engineering and Mechanics, Virginia Tech University, USA


Ulrich Sander

Chalmers, Mekanik och maritima vetenskaper, Fordonsteknik och autonoma system

Saving Lives with V2X versus On-Board Sensing Systems -Which will be More Effective?

SAE Technical Papers,; (2012)

Artikel i vetenskaplig tidskrift

Opportunities and limitations for intersection collision intervention - A study of real world ‘left turn across path’ accidents

Accident Analysis and Prevention,; Vol. 99(2017)p. 342-355

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The potential of clustering methods to define intersection test scenarios: Assessing real-life performance of AEB

Accident Analysis and Prevention,; Vol. 113(2018)p. 1-11

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Market penetration of intersection AEB: Characterizing avoided and residual straight crossing path accidents

Accident Analysis and Prevention,; Vol. 115(2018)p. 178-188

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Many road traffic crashes occur at intersections leading to severe injuries or even death. Automated Emergency Braking (AEB) has shown to be very effective in reducing the number of crashes, however, current AEB systems are mainly designed to prevent one vehicle running with the front into the rear-end of a vehicle ahead. Situations where vehicles are on crossing or oncoming paths at intersections and junctions are more complex: First, vehicles may go straight or turn, and driver intentions may become only visible when the vehicles are already close to each other. Second, the activation of an AEB may not be able to avoid the crash, but due to vehicle braking the vehicles may collide with each other in a different way, potentially increasing the risk for occupant injury. Third, when AEB addressing intersection crashes is available in large numbers, both vehicles involved in a conflict are likely to be equipped with the safety system and the systems may influence each other’s performances positively or negatively.

This thesis seeks to shed light on the opportunities and limitations of AEB functionality at intersections: how design specifications influence system performance and how crash avoidance and injury mitigation change with market penetration. Another aspect lies in the deeper understanding of intersection crashes itself: Which characteristics are similar, and which characteristics diverge?

The methods presented in this thesis are taken from different research areas and combined to an interdisciplinary approach. Accident data reconstruction and analysis is used together with virtual simulation of driver behavior, vehicle dynamics and safety system hardware and software. Statistical methods complement the virtual simulation to define the assessment framework called PRAEDICO: I make known.




Transportteknik och logistik





Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4461



KC Kemigården 4

Opponent: Professor H. Clay Gabler, Ph.D., Department of Biomedical Engineering and Mechanics, Virginia Tech University, USA

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