Obtain a Simulation Model of a Pedestrian Collision Imminent Braking System Based on the Vehicle Testing Data

Forward pedestrian collision imminent braking (CIB) systems has proven to be of great significance in improving road safety and protecting pedestrians. Since pedestrian CIB technology is not mature, the performance of different pedestrian CIB systems varies significantly. Therefore the simulation of a CIB system needs to be vehicle specific. The CIB simulation can be based on the component sensor parameters and decision making rules. Since these parameters and decision rules for on the market vehicles are not available outside of vehicle manufactures, it is difficult for the general research communities to develop a good CIB simulation model based on this approach. To solve this problem, this study presents a new method for developing a pedestrian CIB simulation model using pedestrian CIB testing data. The implementation was in PreScan. The simulation results demonstrate that a pedestrian CIB simulation model developed using this methodology could reflect the behavior of a real vehicle equipped with pedestrian CIB system

Analysis of Driver Behavior Modeling in Connected Vehicle Safety Systems Through High Fidelity Simulation

A critical aspect of connected vehicle safety analysis is understanding the impact of human behavior on the overall performance of the safety system. Given the variation in human driving behavior and the expectancy for high levels of performance, it is crucial for these systems to be flexible to various driving characteristics. However, design, testing, and evaluation of these active safety systems remain a challenging task, exacerbated by the lack of behavioral data and practical test platforms. Additionally, the need for the operation of these systems in critical and dangerous situations makes the burden of their evaluation very costly and time-consuming. As an alternative option, researchers attempt to use simulation platforms to study and evaluate their algorithms. In this work, we introduce a high fidelity simulation platform, designed for a hybrid transportation system involving both human-driven and automated vehicles. We decompose the human driving task and offer a modular approach in simulating a large-scale traffic scenario, making it feasible for extensive studying of automated and active safety systems. Furthermore, we propose a human-interpretable driver model represented as a closed-loop feedback controller. For this model, we analyze a large driving dataset to extract expressive parameters that would best describe different driving characteristics. Finally, we recreate a similarly dense traffic scenario within our simulator and conduct a thorough analysis of different human-specific and system-specific factors and study their effect on the performance and safety of the traffic network

Human-Machine Interface Development For Modifying Driver Lane Change Behavior In Manual, Automated, And Shared Control Automated Driving

Rear-end crashes are common on U.S. roads. Driver assistance and automated driving technologies can reduce rear-end crashes (among other crash types as well). Braking is assumed for forward collision warning (FCW) and automatic emergency braking (AEB) systems. Braking is also used for adaptive cruise control (ACC) and in automated driving systems more generally. However, steering may be advised in an emergency if the adjacent lane is clear and braking is unlikely to avoid a collision. Steering around an obstacle when feasible also eliminates the risk of becoming the new forward collision hazard. Driver assist technology like emergency steer assist (ESA) and Level 2 or Level 3 automated driving systems might facilitate manual emergency lane changes but may require the driver to manually initiate the maneuver, something which drivers are often reluctant to do. An Human-Machine Interface (HMI) might advise the driver of a steerable path when feasible in forward collision hazard situations. Such an HMI might also advise a driver of normal lane change opportunities that can reduce travel time, increase fuel efficiency, or simply enhance the driving experience by promoting `flow.\u27 This dissertation investigated the propensity of drivers to brake only versus steer in both manual and automated driving situations that end in a high-intensity forward collision hazard. A audio-visual Field of Safe Travel (FOST) cluster display and haptic steering wheel HMI were developed to advise drivers in both discretionary and emergency situations of a lane change opportunity. The HMI was tested in a moving base simulator in manual driving, in fully autonomous driving, and in shared-control autonomous driving during a simulated highway commute that ended in an high-intensity forward collision hazard situation. Results indicated that a) driver response was affected by the nature of the automated driving (faster response in hands-on shared control versus hands-off fully autonomous driving); b) exposure to the HMI in normal lane changes both familiarized the driver with the HMI and introduced a mental set that steering was also a possibility rather than braking only; c) but that drivers used their direct vision to determine their response in the emergency event. A methodological issue related to mental set was also uncovered and resolved through screening studies. The final study brought the dissertation full-circle, comparing hands-off fully automated driving to hands-on shared control automated driving in the context of either providing some or no exposure to the developed LCA system concept. Results of the final study indicated that shared control lies somewhere between that of manual driving and hands-off fully automate driving. Benefits were also shown to exist for the LCA system concept irrespective of whether the discrete haptic profiles are included or not. The discrete haptic profiles did not statistically reliably increase response times to the FC hazard event, although they do show a trend toward decreasing response variability. This finding solidified the fact that by implementing a system for benign driving that aids in establishing a mental set to steer around an obstacle may actually be beneficial for rear-end crash scenarios. This dissertation’s contributions include a) audio-visual FOST display concepts; b) discrete haptic steering display concepts; c) a paired-comparisons scaling for urgency for haptic displays applied while driving; d) a new ``mirage scenario\u27\u27 methodology for eliciting subjective assessments in the context of a forward collision hazard, briefly presented then removed, without risk of simulator sickness, and e) a methodological lesson for others who wish to investigate semi-automated and automated driving interventions and must manage driver mental set carefully

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

Investigating the effect of urgency and modality of pedestrian alert warnings on driver acceptance and performance

Active safety systems have the potential to reduce the risk to pedestrians by warning the driver and/or taking evasive action to reduce the effects of or avoid a collision. However, current systems are limited in the range of scenarios they can address using primary control interventions, and this arguably places more emphasis in some situations on warning the driver so that they can take appropriate action in response to pedestrian hazards. In a counterbalanced experimental design, we varied urgency (‘when’) based on the time-to-collision (TTC) at which the warning was presented (with associated false-positive alarms, but no false negatives, or ‘misses’), and modality (‘how’) by presenting warnings using audio-only and audio combined with visual alerts presented on a HUD. Results from 24 experienced drivers, who negotiated an urban scenario during twelve 6.0-minute drives in a medium-fidelity driving simulator, showed that all warnings were generally rated ‘positively’ (using recognised subjective ‘acceptance’ scales), although acceptance was lower when warnings were delivered at the shortest (2.0s) TTC. In addition, drivers indicated higher confidence in combined audio and visual warnings in all situations. Performance (based on safety margins associated with critical events) varied significantly between warning onset times, with drivers first fixating their gaze on the hazard, taking their foot off the accelerator, applying their foot on the brake, and ultimately bringing the car to a stop further from the pedestrian when warnings were presented at the longest (5.0s) TTC. In addition, drivers applied the brake further from the pedestrian when combined audio and HUD warnings were provided (compared to audio-only), but only at 5.0s TTC. Overall, the study indicates a greater margin of safety associated with the provision of earlier warnings, with no apparent detriment to acceptance, despite relatively high false alarm rates at longer TTCs. Also, that drivers feel more confident with a warning system present, especially when it incorporates auditory and visual elements, even though the visual cue does not necessarily improve hazard localisation or driving performance beyond the advantages offered by auditory alerts alone. Findings are discussed in the context of the design, evaluation and acceptance of active safety systems

Autonomous Road Vehicle Emergency Obstacle Avoidance Maneuver Framework at Highway Speeds

An Autonomous Road Vehicle (ARV) can navigate various types of road networks using inputs such as throttle (acceleration), braking (deceleration), and steering (change of lateral direction). In most ARV driving scenarios that involve normal vehicle traffic and encounters with vulnerable road users (VRUs), ARVs are not required to take evasive action. This paper presents a novel Emergency Obstacle Avoidance Maneuver (EOAM) methodology for ARVs traveling at higher speeds and lower road surface friction, involving time-critical maneuver determination and control. The proposed EOAM Framework offers usage of the ARV's sensing, perception, control, and actuation system abilities as one cohesive system, to accomplish avoidance of an on-road obstacle, based first on performance feasibility and second on passenger comfort, and is designed to be well-integrated within an ARV high-level system. Co-simulation including the ARV EOAM logic in Simulink and a vehicle model in CarSim is conducted with speeds ranging from 55 to 165 km/h and on road surfaces with friction ranging from 1.0 to 0.1. The results are analyzed and given in the context of an entire ARV system, with implications for future work.Comment: 50 pages, 25 figures, 2 table

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

Safety Evaluation Using Counterfactual Simulations: The use of computational driver behavior models in crash avoidance systems and virtual simulations with optimal subsampling

Traffic safety is a problem worldwide. In-vehicle conflict and crash avoidance systems have been under development and assessment for some time, as integral parts of Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS). Among the methods used to assess conflict and crash avoidance systems developed by the automotive industry, virtual safety assessment methods have been shown to have great potential and efficiency. In fact, scenario generation-based virtual safety assessments play—and are likely to continue to play—a very important role in the assessments of vehicles of all levels of automation. The ultimate aim of this thesis is to improve the safety performance of conflict and crash avoidance systems. This aim is addressed through the use of computational driver models in two different ways. First, by using comfort-zone boundaries in system design, and second, by using a behavior-based crash-causation model together with a novel optimized scenario generation method for virtual safety assessment.The first objective of this thesis is to investigate how a driver model which includes road users’ comfortable behaviors in crash avoidance algorithms impacts the systems’ safety performance and the residual crash characteristics. Chinese car-to-two-wheeler crashes were targeted; Automated Emergency Braking (AEB) algorithms, which comprised the proposed crash avoidance systems, were compared to a traditional AEB algorithm. The proposed algorithms showed larger safety performance benefits. In addition, the similarities in residual crash characteristics regarding impact speed and location after different AEB implementations can potentially simplify the designs of in-crash protection system in future.The second objective is to develop and apply a method for efficient subsampling in crash-causation-model-based scenario generation for virtual safety assessment. The method, which is machine-learning-assisted, actively and iteratively updates the sampling probability based on new simulation results. The crash-causation model is based on off-road glances and a distribution of driver maximum decelerations in critical situations. A simple time-to-collision-based AEB algorithm was used to demonstrate the assessment process as well as the benefits of combining crash-causation-model-based scenario generation and optimal subsampling. The sampling methods are designed to target specific safety benefit indicators, such as impact speed reduction and crash avoidance rate. The results of the study show that the proposed sampling method requires almost 50% fewer simulations than traditional importance sampling.Future work aims to focus on applying the active sampling method to driver-model-based car-to-vulnerable road user (VRU) scenario generation. In addition to assessing conflict and crash avoidance system performance, a novel stopping criterion based on Bayesian future prediction will be further developed and demonstrated for use in experiments (e.g., as part of developing driver models) and virtual simulations (e.g., using driver-behavior-based crash-causation models). This criterion will be able to indicate when studies are unlikely to yield actionable results within the budget available, facilitating the decision to discontinue them while they are being run