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

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

Design and evaluation of safety-critical applications based on inter-vehicle communication

Inter-vehicle communication has a potential to improve road traffic safety and efficiency. Technical feasibility of communication between vehicles has been extensively studied, but due to the scarcity of application-level research, communication\u27s impact on the road traffic is still unclear. This thesis addresses this uncertainty by designing and evaluating two fail-safe applications, namely, Rear-End Collision Avoidance and Virtual Traffic Lights

Analyzing and modelling drivers’ deceleration behaviour from normal driving

Most research in vehicle automation has mainly focused on the safety aspect with only limited studies on occupants’ discomfort. In order to facilitate their rapid uptake and penetration, autonomous vehicles (AVs) should ensure that occupants are both safe and comfortable. Recent research however revealed that people felt uncomfortable when AVs braked. This may be due to their robot-like braking performance. Existing studies on drivers’ braking behaviour investigated data either from controlled experiments or driving simulators. There is a dearth of research on braking behaviour in normal driving. The objective of this paper is therefore to examine drivers’ braking behaviours by exploiting naturalistic driving data from the Pan-European TeleFOT (Field Operational Tests of Aftermarket and Nomadic Devices in Vehicles) project. On a fixed route of 16.5km long, 16 drivers were asked to drive an instrumented vehicle. A total of about eleven million observations were analysed to identify the profile, value and duration of deceleration events. Since deceleration events are nested within trips and trips within drivers, multilevel mixed-effects linear models were employed to develop relationships between deceleration value and duration and the factors influencing them. The results indicate that the most used profile of the deceleration behaviour follows a hard braking at the beginning when detecting a danger and then becomes smoother. Furthermore, they suggest that the speed, the reason for braking and the deceleration profile mostly affect the deceleration events. Findings from this study should be considered in examining the braking behaviour of AVs

An Overview on Study of Identification of Driver Behavior Characteristics for Automotive Control

Driver characteristics have been the research focus for automotive control. Study on identification of driver characteristics is provided in this paper in terms of its relevant research directions and key technologies involved. This paper discusses the driver characteristics based on driver’s operation behavior, or the driver behavior characteristics. Following the presentation of the fundamental of the driver behavior characteristics, the key technologies of the driver behavior characteristics are reviewed in detail, including classification and identification methods of the driver behavior characteristics, experimental design and data acquisition, and model adaptation. Moreover, this paper discusses applications of the identification of the driver behavior characteristics which has been applied to the intelligent driver advisory system, the driver safety warning system, and the vehicle dynamics control system. At last, some ideas about the future work are concluded

Safety-critical scenarios and virtual testing procedures for automated cars at road intersections

This thesis addresses the problem of road intersection safety with regard to a mixed population of automated vehicles and non-automated road users. The work derives and evaluates safety-critical scenarios at road junctions, which can pose a particular safety problem involving automated cars. A simulation and evaluation framework for car-to-car accidents is presented and demonstrated, which allows examining the safety performance of automated driving systems within those scenarios. Given the recent advancements in automated driving functions, one of the main challenges is safe and efficient operation in complex traffic situations such as road junctions. There is a need for comprehensive testing, either in virtual testing environments or on real-world test tracks. Since it is unrealistic to cover all possible combinations of traffic situations and environment conditions, the challenge is to find the key driving situations to be evaluated at junctions. Against this background, a novel method to derive critical pre-crash scenarios from historical car accident data is presented. It employs k-medoids to cluster historical junction crash data into distinct partitions and then applies the association rules algorithm to each cluster to specify the driving scenarios in more detail. The dataset used consists of 1,056 junction crashes in the UK, which were exported from the in-depth On-the-Spot database. The study resulted in thirteen crash clusters for T-junctions, and six crash clusters for crossroads. Association rules revealed common crash characteristics, which were the basis for the scenario descriptions. As a follow-up to the scenario generation, the thesis further presents a novel, modular framework to transfer the derived collision scenarios to a sub-microscopic traffic simulation environment. The software CarMaker is used with MATLAB/Simulink to simulate realistic models of vehicles, sensors and road environments and is combined with an advanced Monte Carlo method to obtain a representative set of parameter combinations. The analysis of different safety performance indicators computed from the simulation outputs reveals collision and near-miss probabilities for selected scenarios. The usefulness and applicability of the simulation and evaluation framework is demonstrated for a selected junction scenario, where the safety performance of different in-vehicle collision avoidance systems is studied. The results show that the number of collisions and conflicts were reduced to a tenth when adding a crossing and turning assistant to a basic forward collision avoidance system. Due to its modular architecture, the presented framework can be adapted to the individual needs of future users and may be enhanced with customised simulation models. Ultimately, the thesis leads to more efficient workflows when virtually testing automated driving at intersections, as a complement to field operational tests on public roads

Design and evaluation of CCA (Cooperative Collision Avoidance) applications for vehicular ad-hoc networks

[SPA] El tema central de la Tesis ha versado sobre el diseño y evaluación de aplicaciones para la reducción de la probabilidad de colisión en carretera mediante el uso de conectividad inalámbrica entre vehículos, particularmente en un escenario específico del tráfico rodado: presencia de un obstáculo en la dirección de tránsito que bloquea el paso. Dos enfoques han sido tomados en consideración: utilización de mecanismos de anticipación cooperativa vehículo a vehículo para evadir colisiones mediante frenada, y empleo de esquemas de maniobras de evasión cooperativa en circunstancias donde existe suficiente espacio en la carretera para reorientar las trayectorias y evitar el choque. Se ha hecho uso de herramientas de simulación de redes y dinámica vehicular, y de la teoría matemática de la optimización y de los procesos estocásticos para modelar estos escenarios. Los resultados demuestran que el uso de comunicaciones, junto con sistemas avanzados de inteligencia artificial permitirá en un futuro garantizar cotas de seguridad en carretera nunca antes vistas, incluso en situaciones de riesgo extremo que podrían ser detectadas por uno o más vehículos con tiempos muy cortos de reacción.[ENG] New emerging technologies in vehicular traffic are aimed primarily at improving safety and driving comfort for passengers, by paying special attention to the gradual evermore automation of all aspects of the driving task. In this regard, a promising research perspective considered by the Academia and the Industry is to use communications to build a complex interoperable vehicular network that would serve as a means to provide autonomous robotic-guided vehicles with additional status information that might not be collected from sensors on board. With properly configured processing schemes, this additional stream of information can be used to help vehicles anticipate and react conveniently to potentially risky situations that might cause an accident if not previously considered. Particularly, in this Thesis we use these premises to propose and evaluate collision avoidance policies under two specific fashions: i) Design and evaluation of a Cooperative chain Collision Avoidance (CcCA)1 strategy to reduce the impact of multiple rear-end collisions in a platoon of vehicles when evasive maneuvering is not possible, and ii) Analysis and optimization of different strategies for Cooperative Collision Avoidance (CCA) by evasive maneuvering. The CcCA application allows us to study how communication protocols, both by one-hop transmissions as well as by relaying (multi-hop) schemes, can help reduce the number of accidents, or at least minimize their impact, in cases where vehicles cannot execute sudden maneuvers to skip cars ahead, but only brake. Simulations are validated by using an advanced stochastic model which rigorously describes the behavior of vehicles in this type of situations. Among other aspects, results show that real implementations of CcCA must take into account with special relevance those vehicles that might be humanly driven, and guarantee that during the transition stage (until a complete penetration of the technology is achieved) safety is preserved enough. Regarding CCA for evasive maneuvering, we provide an exhaustive optimization analysis for the calculation of optimum trajectories in cases where vehicles at high speeds are at risk of colliding with one or more obstacles appearing ahead. By reorienting trajectories through the lateral free spaces that might exist between the obstacles and the crash barriers (if the specific scenario allows it), vehicles can avoid crashing and simultaneously improve driving comfort even under such unpredictable circumstances. On the whole, despite much further effort is still required on these matters, results in this Work show that communications can help autonomous vehicles to make decisions in a cooperative fashion that will not only assist individuals to follow the best riding strategy, but also the traffic system as a whole to evolve according to the best possible behavior in terms of safety and comfort.Universidad Politécnica de CartagenaPrograma de doctorado en Tecnologías de la Información y Comunicacione