Understanding and modelling car drivers overtaking cyclists: Toward the inclusion of driver models in virtual safety assessment of advanced driving assistance systems

The total number of road crashes in Europe is decreasing, but the number of crashes involving cyclists is not decreasing at the same rate. To help car drivers avoid or mitigate crashes while overtaking a cyclist, advanced driver assistance systems (ADAS) have been developed. To evaluate and further improve these ADAS to support drivers as they overtake cyclists, we need to understand and model driver behaviours.This thesis has two objectives: 1) to extract and analyse cyclist-overtaking manoeuvres from naturalistic driving data and 2) compare driver behaviour models for overtaking manoeuvres that can be used in counterfactual simulations for evaluating ADAS safety benefits.The drivers’ comfort zone boundaries (CZBs) when overtaking a cyclist were identified and analysed using naturalistic driving data. Three driver models that predict when a car driver starts steering away in order to overtake a cyclist were implemented: a threshold model, an evidence accumulation model, and a model inspired by a proportional-integral-derivative controller. These models were tested and verified using two different datasets, one from a test-track experiment and one from naturalistic driving data. Model parameters were obtained using computationally efficient linear programming.The results show that, when an oncoming vehicle was present, the drivers were significantly closer to the cyclist before steering away. This finding indicates that the presence of an oncoming vehicle is a crucial factor for the safety of the cyclist and needs to be taken into account for the development of ADAS that maintain safe distance to the cyclist. Furthermore, the quantification of the CZBs has implications for the development of ADAS which can estimate the time-to-collision to an oncoming vehicle or a cyclist to be overtaken, providing timely and acceptable warnings—or interventions—when drivers exceed their usual CZBs. A comparison of the models shows that all three are highly variable in detecting steering away time for different drivers. Furthermore, differences were discovered in detected steering away time between models fitted to test-track experiments and naturalistic driving data. Future work may focus on using larger, more diverse datasets and investigating more advanced models before including them in counterfactual simulations

A Holistic Safety Benefit Assessment Framework for Heavy Goods Vehicles

In 2019, more than one million crashes occurred on European roads, resulting in almost 23,000 traffic fatalities. Although heavy goods vehicles (HGVs) were only involved in 4.4% of these crashes, their proportion in crashes with fatal outcomes was almost three times larger. This over-representation of HGVs in fatal crashes calls for actions that can support the efforts to realize the vision of zero traffic fatalities in the European Union. To achieve this vision, the development and implementation of passive as well as active safety systems are necessary. To prioritise the most effective systems, safety benefit estimations need to be performed throughout the development process. The overall aim of this thesis is to provide a safety benefit assessment framework, beyond the current state of the art, which supports a timely and detailed assessment of safety systems (i.e. estimation of the change in crash and/or injury outcomes in a geographical region), in particular active safety systems for HGVs. The proposed framework is based on the systematic integration of different data sources (e.g. virtual simulations and physical tests), using Bayesian statistical methods to assess the system performance in terms of the number of lives saved and injuries avoided. The first step towards the implementation of the framework for HGVs was an analysis of three levels of crash data that identified the most common crash scenarios involving HGVs. Three scenarios were recognized: HGV striking the rear-end of another vehicle, HGV turning right in conflict with a cyclist, and HGV in conflict with a pedestrian crossing the road. Understanding road user behaviour in these critical scenarios was identified as an essential element of an accurate safety benefit assessment, but sufficiently detailed descriptions of HGV driver behaviour are currently not available. To address this research gap, a test-track experiment was conducted to collect information on HGV driver behaviour in the identified cyclist and pedestrian target scenarios. From this information, HGV driver behaviour models were created. The results show that the presence of a cyclist or pedestrian creates different speed profiles (harder braking further away from the intersection) and changes in the gaze behaviours of the HGV drivers, compared to the same situation where the vulnerable road users are not present. However, the size of the collected sample was small, which posed an obstacle to the development of meaningful driver models. To overcome this obstacle, a framework to create synthetic populations through Bayesian functional data analysis was developed and implemented. The resulting holistic safety benefit assessment framework presented in this thesis can be used not only in future studies that assess the effectiveness of safety systems for HGVs, but also during the actual development process of advanced driver assistance systems. The research results have potential implications for policies and regulations (such as new UN regulations for mandatory equipment or Euro NCAP ratings) which are based on the assessment of the real-world benefit of new safety systems and can profit from the holistic safety benefit assessment framework

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

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