Making overtaking cyclists safer: Driver intention models in threat assessment and decision-making of advanced driver assistance system

Introduction: The number of cyclist fatalities makes up 3% of all fatalities globally and 7.8% in the European Union. Cars overtaking cyclists on rural roads are complex situations. Miscommunication and misunderstandings between road users may lead to crashes and severe injuries, particularly to cyclists, due to lack of protection. When making a car overtaking a cyclist safer, it is important to understand the interaction between road users and use in the development of an Advanced Driver Assistance System (ADAS). Methods: First, a literature review was carried out on driver and interaction modeling. A Unified Modeling Language (UML) framework was introduced to operationalize the interaction definition to be used in the development of ADAS. Second, the threat assessment and decision-making algorithm were developed that included the driver intention model. The counterfactual simulation was carried out on artificial crash data and field data to understand the intention-based ADAS\u27s performance and crash avoidance compared to a conventional system. The method focused on cars overtaking cyclists when an oncoming vehicle was present. Results: An operationalized definition of interaction was proposed to highlight the interaction between road users. The framework proposed uses UML diagrams to include interaction in the existing driver modeling approaches. The intention-based ADAS results showed that using the intention model, earlier warning or emergency braking intervention can be activated to avoid a potential rear-end collision with a cyclist without increasing more false activations than a conventional system. Conclusion: The approach used to integrate the driver intention model in developing an intention-based ADAS can improve the system\u27s effectiveness without compromising its acceptance. The intention-based ADAS has implications towards reducing worldwide road fatalities and in achieving sustainable development goals and car assessment program

How do drivers negotiate intersections with pedestrians? The importance of pedestrian time-to-arrival and visibility

Forward collision warning (FCW) and autonomous emergency braking (AEB) systems are increasingly available and prevent or mitigate collisions by alerting the driver or autonomously braking the vehicle. Threat-assessment and decision-making algorithms for FCW and AEB aim to find the best compromise for safety by intervening at the “right” time: neither too early, potentially upsetting the driver, nor too late, possibly missing opportunities to avoid the collision.Today, the extent to which activation times for FCW and AEB should depend on factors such as pedestrian speed and lane width is unknown. To guide the design of FCW and AEB intervention time, we employed a fractional factorial design, and determined how seven factors (crossing side, car speed, pedestrian speed, crossing angle, pedestrian size, zebra-crossing presence, and lane width) affect the driver’s response process and comfort zone when negotiating an intersection with a pedestrian. Ninety-four volunteers drove through an intersection in a fixed-base driving simulator, which was based on open-source software (OpenDS). Several parameters, including pedestrian time-to-arrival and driver response time, were calculated to describe the driver response process and define driver comfort boundaries.Linear mixed-effect models showed that driver responses depended mainly on pedestrian time-to-arrival and visibility, whereas factors such as pedestrian size, zebra-crossing presence, and lane width did not significantly influence the driver response process. Some drivers changed their negotiation strategy (proportion of pedal braking to engine braking) to minimize driving effort over the course of the experiment. Experienced drivers changed more than less experienced drivers; nevertheless, all drivers behaved similarly, independent of driving experience. The flexible and customizable driving environment provided by OpenDS may be a viable platform for behavioural experiments in driving simulators.Results from this study suggest that visibility and pedestrian time-to-arrival are the most important variables for defining the earliest acceptable FCW and AEB activations. Fractional factorial design effectively compared the influence of several factors on driver behaviour within a single experiment; however, this design did not allow in-depth data analysis. In the future, OpenDS might become a standard platform, enabling crowdsourcing and favouring repeatability across studies in traffic safety. Finally, this study advises future design and evaluation procedures (e.g. new car assessment programs) for FCW and AEB by highlighting which factors deserve further investigation and which ones do not

How do oncoming traffic and cyclist lane position influence cyclist overtaking by drivers?

Overtaking cyclists is challenging for drivers because it requires a well-timed, safe interaction between the driver, the cyclist, and the oncoming traffic. Previous research has investigated this manoeuvre in different experimental environments, including naturalistic driving, naturalistic cycling, and simulator studies. These studies highlight the significance of oncoming traffic—but did not extensively examine the influence of the cyclist’s position within the lane. In this study, we performed a test-track experiment to investigate how oncoming traffic and position of the cyclist within the lane influence overtaking. Participants overtook a robot cyclist, which was controlled to ride in two different lateral positions within the lane. At the same time, an oncoming robot vehicle was controlled to meet the participant’s vehicle with either 6 or 9 s time-to-collision. The order of scenarios was randomized over participants. We analysed safety metrics for the four different overtaking phases, reflecting drivers’ safety margins to rear-end, head-on, and side-swipe collisions, in order to investigate the two binary factors: 1) time gap between ego vehicle and oncoming vehicle, and 2) cyclist lateral position. Finally, the effects of these two factors on the safety metrics and the overtaking strategy (either flying or accelerative depending on whether the overtaking happened before or after the oncoming vehicle had passed) were analysed. The results showed that, both when the cyclist rode closer to the centre of the lane and when the time gap to the oncoming vehicle was shorter, safety margins for all potential collisions decreased. Under these conditions, drivers—particularly female drivers—preferred accelerative over flying manoeuvres. Bayesian statistics modelled these results to inform the development of active safety systems that can support drivers in safely overtaking cyclists

Modelling discomfort: How do drivers feel when cyclists cross their path?

Introduction: Even as worldwide interest in bicycling continues to grow, cyclists constitute a large part of road fatalities. A major part of the fatalities occurs when cyclists cross a vehicle path. Active safety systems and automated driving systems may already account for these interactions in their control algorithms. However, the driver behaviour models that these systems use may not be optimal in terms of driver acceptance. If the systems could estimate driver discomfort, their acceptance might be improved.Method: This study investigated the degree of discomfort experienced by drivers when cyclists crossed their travel path. Participants were instructed to drive through an intersection in a fixed-base simulator or on a test track, following the same experimental protocol. The effects of demographic variables (age, gender, driving frequency, and yearly mileage), controlled variables (car speed, bicycle speed, and bicycle-car configuration), and a visual cue (car’s time-to-arrival at the intersection when the bicycle appears; TTAvis) on self-reported discomfort were analysed using cumulative link mixed models (CLMM).Results: Results showed that demographic variables had a significant effect on the discomfort felt by drivers—and could explain the variability observed between drivers. Across both experimental environments, the controlled variables were shown to significantly influence discomfort. TTAvis was shown to have a significant effect on discomfort as well; the closer to zero TTAvis was (i.e., the more critical the situation), the more likely the driver red great discomfort. The prediction accuracies of the CLMM with controlled variables and the CLMM with the visual cue were similar, with an average accuracy between 40 and 50%. Surprise trials in the simulator experiment, in which the bicycle appeared unexpectedly, improved the prediction accuracy of the models, more notably the CLMM including TTAvis. Conclusions: The results suggest that the discomfort was mainly driven by the visual cue rather than the deceleration cues. Thus, it is suggested that an algorithm that estimates driver discomfort be included in active safety systems and autonomous driving systems. The CLMM including TTAvis was presented as a potential candidate to serve this purpose

A new framework for modelling road-user interaction and evaluating active safety systems

In the road transport system, cyclists account for a significant share of fatalities and serious injuries. Advance driver assistance systems (ADAS) that address potential crashes of passenger cars with cyclists are being developed and introduced to the market. Safety benefit evaluation of these ADAS is important to verify if current ADAS actually reduce real-world crashes and to determine the extent to which novel algorithms may improve current ADAS, before entering the market. ADAS safety benefit evaluation requires an agreed framework with defined concepts for target scenario specification, models of road‑user behaviour and road‑user interaction, and performance metrics

Modelling Interaction between Cyclists and Automobiles - Final Report

The MICA project modelled driver behaviour, focusing on the approaching phase of an overtaking manoeuvre, when a driver moved toward a cyclist while facing oncoming traffic (Euro NCAP test protocols inspired this scenario.). The model predicts the probability for drivers to brake or steer as they approach the cyclists to perform an accelerative (overtake after the oncoming traffic has passed) or flying (overtake before the oncoming traffic has passed) manoeuvre, respectively. This model has been integrated into a smart collision-avoidance system, that provides early (and yet acceptable) warnings and interventions. A virtual assessment estimated the safety benefits of the smart collision-avoidance system using UDRIVE naturalistic data. Our analyses show that the new smart collision-avoidance system can significantly reduce fatalities and severe injuries when compared to traditional collision-avoidance systems, with the new collision warning alone promising a reduction of fatalities by 53-96% and a reduction of serious injuries by 43-93%. This work has been carried out by three PhD students and is now continuing in the MICA2 project.\ua0The main deliverables of the project were:1)\ua0\ua0\ua0\ua0\ua0 a unique dataset collected on the airfield in V\ue5rg\ue5rda where participants interacted with two robots, 2)\ua0\ua0\ua0\ua0\ua0 a new modelling framework that helps to identify interaction on a scenario basis,3)\ua0\ua0\ua0\ua0\ua0 a novel driver model, which can predict overtaking strategy in real-time, 4)\ua0\ua0\ua0\ua0\ua0 a smart collision-avoidance system which uses the driver model to generate warnings and automated interventions, and5)\ua0\ua0\ua0\ua0\ua0 a safety benefit analysis, proving the potential for the new collision-avoidance systems to save lives and reduce injuries from naturalistic European data. \ua0Nine scientific contributions describe MICA’s results: one licentiate thesis, two podium presentations to the International Cycling Safety Conference (2018 and 2019, respectively), one conference paper submitted to the Transport Research Arena 2020, and five journal papers.\ua0MICA highlighted that:1)\ua0\ua0\ua0\ua0\ua0 Modelling the interaction between the overtaking vehicle and the oncoming vehicle is an essential step to increase overtaking safety.2)\ua0\ua0\ua0\ua0\ua0 The approaching phase of an overtaking manoeuvre is not necessarily the riskiest; the most significant margin for improving safety may lay in developing systems that support the drivers in the returning phase.3)\ua0\ua0\ua0\ua0\ua0 In the approaching phase of an overtaking manoeuvre, the potential safety benefits from automated emergency steering (a system not addressed in MICA) is substantial.4)\ua0\ua0\ua0\ua0\ua0 As an overtaking manoeuvre develops from the approaching to the steering, passing, and returning phase, vehicle kinematics and proximities become more critical, challenging active safety systems and calling for new passive safety solutions.5)\ua0\ua0\ua0\ua0\ua0 More experimental data, collected in more critical situations than what was possible in MICA, is needed to address overtaking safety properly. New methodologies, such as augmented reality and virtual reality, offer the best opportunities to collect such data without ethical concerns.6)\ua0\ua0\ua0\ua0\ua0 More naturalistic data is needed to validate our driver models and the new systems that we started developing in MICA.7)\ua0\ua0\ua0\ua0\ua0 Interaction among road users is complex and models of vulnerable road-user behaviour are also needed to make robust predictions. As we move from an overtaking scenario to a crossing scenario, this aspect will become even more crucial.\ua0MICA2, a new FFI project including Volvo Cars, Autoliv, Veoneer, Viscando, if, VTI, and Chalmers, will now address these issues

How do drivers negotiate intersections with pedestrians? Fractional factorial design in an open-source driving simulator

Forward collision warning (FCW) and autonomous emergency braking (AEB) systems are increasingly available and promise to prevent or mitigate collisions by alerting the driver or autonomously braking the vehicle. Threat-assessment and decision-making algorithms for FCW and AEB aim to find the best compromise for safety by intervening at the “right” time: neither too early, potentially upsetting the driver, nor too late, possibly missing opportunities to avoid the collision.Today, the extent to which intervention times for FCW and AEB should depend on factors such as pedestrian speed and lane width is unknown. To guide the design of FCW and AEB intervention time, we employed a fractional factorial design, and determined how seven factors (crossing side, car speed, pedestrian speed, crossing angle, pedestrian size, zebra presence, and lane width) affect the driver’s response process and comfort zone when negotiating an intersection with a pedestrian. Ninety-four volunteers drove through an intersection in a fixed-base driving simulator, which was based on open-source software (OpenDS). Several parameters, including pedestrian time-to-arrival and driver response time, were calculated to describe the driver response process and define driver comfort boundaries.Linear mixed-effect models showed that driver responses depended mainly on pedestrian time-to-arrival and visibility, whereas factors such as pedestrian size, zebra presence, and lane width did not significantly influence the driver response process. Some drivers changed their negotiation strategy to minimize driving effort over the course of the experiment. Experienced drivers changed more than less experienced drivers; nevertheless, all drivers behaved similarly, independent of driving experience. The flexible and customizable driving environment provided by OpenDS proved to be a viable solution for behavioural experiments in driving simulators.Results from this study suggest that visibility and pedestrian time-to-arrival are the most important factors for defining the earliest acceptable FCW and AEB activations. Fractional factorial design effectively compared the influence of several factors on driver behaviour within a single experiment; however, this design did not allow in-depth data analysis. In the future, OpenDS may became a standard platform, enabling crowdsourcing and favouring repeatability across studies in traffic safety. Finally, this study may guide future design and evaluation of FCW and AEB by highlighting which factors deserve further investigation and which ones do not

Modelling how drivers respond to a bicyclist crossing their path at an intersection: How do test track and driving simulator compare?

Bicyclist fatalities are a great concern in the European Union. Most of them are due to crashes between motorized vehicles and bicyclists at unsignalised intersections. Different countermeasures are currently being developed and implemented in order to save lives. One type of countermeasure, active safety systems, requires a deep understanding of driver behaviour to be effective without being annoying. The current study provides new knowledge about driver behaviour which can inform assessment programmes for active safety systems such as Euro NCAP. This study investigated how drivers responded to bicyclists crossing their path at an intersection. The influences of car speed and cyclist speed on the driver response process were assessed for three different crossing configurations. The same experimental protocol was tested in a fixed-base driving simulator and on a test track. A virtual model of the test track was used in the driving simulator to keep the protocol as consistent as possible across testing environments. Results show that neither car speed nor bicycle speed directly influenced the response process. The crossing configuration did not directly influence the braking response process either, but it did influence the strategy chosen by the drivers to approach the intersection. The point in time when the bicycle became visible (which depended on the car speed, the bicycle speed, and the crossing configuration) and the crossing configuration alone had the largest effects on the driver response process. Dissimilarities between test-track and driving-simulator studies were found; however, there were also interesting similarities, especially in relation to the driver braking behaviour. Drivers followed the same strategy to initiate braking, independent of the test environment. On the other hand, the test environment affected participants\u27 strategies for releasing the gas pedal and regulating deceleration. Finally, a mathematical model, based on both experiments, is proposed to characterize driver braking behaviour in response to bicyclists crossing at intersections. This model has direct implications on what variables an in-vehicle safety system should consider and how tests in evaluation programs should be designed