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

Methods and models for safety benefit assessment of advanced driver assistance systems in car-to-cyclist conflicts

To help drivers avoid or mitigate the severity of crashes, advanced driver assistance systems (ADAS) can be designed to provide warnings or interventions. Prospective safety assessment of ADAS is important to quantify and optimise their safety benefit. Such safety assessment methods include, for example, virtual simulations and test-track testing.Today, there are many components of virtual safety assessment simulations with models or methods that are missing or can be substantially improved. This is particularly true for simulations assessing ADASs that address crashes involving cyclists—a crash type that is not decreasing at the same rate as the overall number of road crashes in Europe. The specific methodological gaps that this work addresses are: a) computational driver models for car-to-cyclist overtaking, b) algorithms for model fitting and efficient calculation of ADAS intervention time, and c) a method for merging data from different data sources into the safety assessment.Specifically, for a), different driver models for everyday driver behaviour while overtaking cyclists in a naturalistic driving setting were derived and compared. For b), computationally efficient algorithms to fit driver models to data and compute ADAS intervention time were developed for different types of vehicle models. The algorithms can be included in ADAS both for offline use in virtual assessment simulations and online real-time use in in-vehicle ADAS. Lastly, for c), a method was developed that uses Bayesian statistics to combine results from different data sources, e.g., simulations and test-track data, for ADAS safety benefit assessment.In addition to presenting five peer-reviewed scientific publications, which address these issues, this compilation thesis discusses the use of different data sources; introduces the fundamentals of Bayesian inference, linear programming, and numerical root-finding algorithms; and provides the rationale for methodological choices made, where relevant. Finally, this thesis describes the relationships among the publications and places them into context with existing literature.This work developed driver models for the virtual simulations and methods for the reliable estimation of the prospective safety benefit, which together have the potential to improve the design and the evaluation of ADAS in general, and ADAS for the car-to-cyclist overtaking scenario in particular

On the evaluation of visual nudges to promote safe cycling: Can we encourage lower speeds at intersections?

Crashes between cars and cyclists at urban intersections are common, and their consequences are often severe. Typical causes for this type of crashes included the excessive speed of the cyclist as well as car drivers failing to see the cyclist. Measures that decrease the cyclists’ speed may lead to safer car-cyclist interactions. This study aimed to investigate the extent to which cyclists may approach intersections at a lower speed when nudged to do so.Visual flat-stripe nudges were placed on bicycle lanes in the proximity of uncontrolled intersections (with a history of car-cyclist crashes) in two locations in Gothenburg, Sweden. This specific nudge was the one obtaining the best results from a previous study that tested different nudges in controlled experiments. Video data from the intersections were recorded with a site-based video recording system both before (baseline), and after (treatment), the nudge was installed.\ua0 The video data was processed to extract trajectory and speed for cyclists. The baseline and treatment periods were equivalent in terms of day of the week, light, and weather conditions. Furthermore, two treatment periods were recorded to capture the effect of the nudge over time in one of the locations.Leisure cyclists showed lower speeds in treatment than in baseline for both locations. Commuters were less affected by the nudge than leisure cyclists. This study shows that visual nudges to decrease cyclist speed at intersections are hard to evaluate in the wild because of the many confounders. We also found that the effect of visual nudges may be smaller than the effect of environmental factors such as wind and demographics, making their evaluation even harder. The observed effect of speed might not be very high, but the advantage both in terms of cyclist acceptance and monetary cost makes an investment in the measure very low risk. This study informs policymakers and road authorities that want to promote countermeasures to intersection crashes and improve the safety of cyclists at urban intersections

A method for identifying aggressive driving by using naturalistic driving data

Aggressive driving has been associated as one of the causes for crashes, sometimes with very serious consequences. By understanding the behavior of the drivers and finding quantitative ways to categorize the behavior associated with higher crash risk, programs for modifying driver behavior towards safer driving can be designed. The objective of this study is to identify aggressive drivers by metrics calculated from naturalistic driving data. The drivers are separated by the aggressive behavior of following too closely to a front vehicle, i.e. tailgating. Furthermore, two jerk metrics are calculated to identify aggressive drivers: a) number of large positive jerks when pressing the gas pedal and b) number of large negative jerks when pressing the brake pedal. Moreover, drivers’ gender, Arnett Inventory of Sensation Seeking (AISS) score, Driver Behavior Questionnaires (DBQ) and country effects on the metrics are analyzed.The results show that the aggressive drivers, defined for car following situations using tailgating metric, were associated with significantly higher frequency of using large negative jerk. The results could be potentially applied in programs for driver training and education, advanced driver coaching, and in the context of usage-based insurance

Modeling collision avoidance maneuvers for micromobility vehicles

Introduction: In recent years, as novel micromobility vehicles (MMVs) have hit the market and rapidly gained popularity, new challenges in road safety have arisen, too. There is an urgent need for validated models that comprehensively describe the behaviour of such novel MMVs. This study aims to compare the longitudinal and lateral control of bicycles and e-scooters in a collision- avoidance scenario from a top-down perspective, and to propose appropriate quantitative models for parameterizing and predicting the trajectories of the avoidance—braking and steering— maneuvers. Method: We compared a large e-scooter and a light e-scooter with a bicycle (in assisted and non-assisted modes) in field trials to determine whether these new vehicles have different maneuverability constraints when avoiding a rear-end collision by braking and/or steering. Results: Braking performance in terms of deceleration and jerk varies among the different types of vehicles; specifically, e-scooters are not as effective at braking as bicycles, but the large e-scooter demonstrated better braking performance than the light one. No statistically significant difference was observed in the steering performance of the vehicles. Bicycles were perceived as more stable, maneuverable, and safe than e-scooters. The study also presents arctangent kinematic models for braking and steering, which demonstrate better accuracy and informativeness than linear models. Conclusions: This study demonstrates that the new micromobility solutions have some maneuverability characteristics which differ significantly from those of bicycles, and even within their own kind. Steering could be a more efficient collision- avoidance strategy for MMVs than braking under certain circumstances, such as in a rear-end collision. More complicated modelling for MMV kinematics can be beneficial but needs validation. Practical Applications: The proposed arctangent models could be used in new advanced driving assistance systems to prevent crashes between cars and MMV users. Micromobility safety could be improved by educating MMV riders to adapt their behavior accordingly. Further, knowledge about the differences in maneuverability between e-scooters and bicycles could inform infrastructure design, and traffic regulations

Modeling the Braking Behavior of Micro-Mobility Vehicles

According to the community database on accidents on the roads in Europe, 2035 cyclist fatalities happened in Europe in 2019 [S]. In Sweden, 10440 bicycle crashes were reported in the Swedish Traffic Accident Data Acquisition database during 2019, and 30% of the cyclist fatalities were in car-to-cyclist rear-end crashes [6]. Nowadays, new micromobility vehicles (MMVs), for example, e-scooters, and Segways, are becoming more popular. Unlike traditional bicycles, these new MMVs usually have novel designs in appearance, kinematics, operation method, and power source (e.g., electricity-driven/assisted), which bring new hazards to traditional road users [1, 4]. Thus, it is essential to understand and quantify the behavior of the new MMV users to improve road safety

On the importance of driver models for the development and assessment of active safety: A new collision warning system to make overtaking cyclists safer

The total number of road crashes in Europe is decreasing, but the number of crashes involving cyclists is not decreasing at the same rate. When cars and bicycles share the same lane, cars typically need to overtake them, creating dangerous conflicts—especially on rural roads, where cars travel much faster than cyclists. In order to protect cyclists, advanced driver assistance systems (ADAS) are being developed and introduced to the market. One of them is a forward collision warning (FCW) system that helps prevent rear-end crashes by identifying and alerting drivers of threats ahead. The objective of this study is to assess the relative safety benefit of a behaviour-based (BB) FCW system that protects cyclists in a car–to–cyclist overtaking scenario. Virtual safety assessments were performed on crashes derived from naturalistic driving data. A series of driver response models was used to simulate different driver reactions to the warning. Crash frequency in conjunction with an injury risk model was used to estimate the risk of cyclist injury and fatality. The virtual safety assessment estimated that, compared to no FCW, the BB FCW could reduce cyclists’ fatalities by 53–96% and serious injuries by 43–94%, depending on the driver response model. The shorter the driver’s reaction time and the larger the driver’s deceleration, the greater the benefits of the FCW. The BB FCW also proved to be more effective than a reference FCW based on the Euro NCAP standard test protocol. The findings of this study demonstrate the BB FCW’s great potential to avoid crashes and reduce injuries in car–to–cyclist overtaking scenarios, even when the driver response model did not exceed a comfortable rate of deceleration. The results suggest that a driver behaviour model integrated into ADAS collision threat algorithms can provide substantial safety benefits

Skademekanismer vid cykelolyckor som resulterat i frakturer

Cyclists account for a large share of injured road users in traffic.\ua0 The crash data analysis for cyclist safety and protection should be based on a representative dataset of real-world crashes. This project aimed to explore the patterns of cyclists’ fractures and factors associated with fractures of higher severity.This project exemplifies a methodology that combines injuries from a crash database, including both hospital and police reports and fracture registry database from orthopaedic centres nationally in Sweden. The results of this study may guide the design of appropriate protective devices for the cyclists based on the different injury mechanisms and provide implications for prioritizing new countermeasures, campaigns, or regulations

A comparison of computational driver models using naturalistic and test-track data from cyclist-overtaking manoeuvres

The improvement of advanced driver assistance systems (ADAS) and their safety assessment rely on the understanding of scenario-dependent driving behaviours, such as steering to avoid collisions. This study compares driver models that predict when a driver starts steering away to overtake a cyclist on rural roads. The comparison is among four models: a threshold model, an accumulator model, and two models inspired by a proportional-integral and proportional-integral-derivative controller. These models were tested and cross-applied using two different datasets: one from a naturalistic driving (ND) study and one from a test-track (TT) experiment. Two perceptual variables, expansion rate (the horizontal angular expansion rate of the image of the lead road user on the driver’s retina) and inverse tau (the ratio between the image’s expansion rate and its horizontal optical size), were tested as input to the models. A linear cost function is proposed that can obtain the optimal parameters of the models by computationally efficient linear programming. The results show that the models based on inverse tau fitted the data better than the models that included expansion rate. In general, the models fitted the ND data reasonably well, but not as well the TT data. For the ND data, the models including an accumulative component outperformed the threshold model. For the TT data, due to the poorer fit of the models, more analysis is required to determine the merit of the models. The models fitted to TT data captured the overall pattern of steering onsets in the ND data rather well, but with a persistent bias, probably due to the drivers employing a more cautious strategy in TT. The models compared in this paper may support the virtual safety assessment of ADAS so that driver behaviour may be considered in the design and evaluation of new safety systems

Mapping fractures from traffic accidents in Sweden: How do cyclists compare to other road users?

Introduction:\ua0Cyclists account for a large share of injured road users in traffic. The crash data analysis for cyclist safety and protection should be based on a representative dataset of real-world crashes. This manuscript aimed to explore the patterns of cyclists’ fractures and factors associated with fractures of higher severity.Methods:\ua0This paper exemplifies a methodology that combines injuries from a crash database, including both hospital and police reports and fracture registry database from orthopedic centers nationally in Sweden.Results:\ua0Car occupants were most frequently involved in crashes resulting in fractures (37%), followed by motorcyclists (27.6%) and bicyclists (15.4%). Common fracture locations differed by the type of road user, where cyclists were more frequently fractured in the lower arm, compared to other road users, such as car drivers, motorcyclists and pedestrians who suffered mostly of fractures in the lower leg. Within cyclists, injuries also differed by gender, suggesting that combination of different countermeasures may be needed in order to provide sufficient protection for all cyclist. In the analyzed data, male cyclists with an average age of 49 were the most frequently fractured cyclists. Fractures of cyclists to the acetabulum (100%), pelvis (84.2%), vertebra (75%) and tibia (70.3%) were most frequently high energy fractures. Single bicycle incidents (OR = 0.165) and collisions with another bicycle (OR = 0.148) were significantly less likely to result in a high energy fracture than a collision with a car.Conclusions:\ua0The results of this study may guide the design of appropriate protective devices for the cyclists based on the different injury mechanisms and provide implications for prioritizing new countermeasures, campaigns, or regulations