Data Collection and Processing Methods for the Evaluation of Vehicle Road Departure Detection Systems

Road departure detection systems (RDDSs) for avoiding/mitigating road departure crashes have been developed and included on some production vehicles in recent years. In order to support and provide a standardized and objective performance evaluation of RDDSs, this paper describes the development of the data acquisition and data post-processing systems for testing RDDSs. Seven parameters are used to describe road departure test scenarios. The overall structure and specific components of data collection system and data post-processing system for evaluating vehicle RDDSs is devised and presented. Experimental results showed sensing system and data post-processing system could capture all needed signals and display vehicle motion profile from the testing vehicle accurately. Test track testing under different scenarios demonstrates the effective operations of the proposed data collection system

Automated driving and autonomous functions on road vehicles

In recent years, road vehicle automation has become an important and popular topic for research and development in both academic and industrial spheres. New developments received extensive coverage in the popular press, and it may be said that the topic has captured the public imagination. Indeed, the topic has generated interest across a wide range of academic, industry and governmental communities, well beyond vehicle engineering; these include computer science, transportation, urban planning, legal, social science and psychology. While this follows a similar surge of interest – and subsequent hiatus – of Automated Highway Systems in the 1990’s, the current level of interest is substantially greater, and current expectations are high. It is common to frame the new technologies under the banner of “self-driving cars” – robotic systems potentially taking over the entire role of the human driver, a capability that does not fully exist at present. However, this single vision leads one to ignore the existing range of automated systems that are both feasible and useful. Recent developments are underpinned by substantial and long-term trends in “computerisation” of the automobile, with developments in sensors, actuators and control technologies to spur the new developments in both industry and academia. In this paper we review the evolution of the intelligent vehicle and the supporting technologies with a focus on the progress and key challenges for vehicle system dynamics. A number of relevant themes around driving automation are explored in this article, with special focus on those most relevant to the underlying vehicle system dynamics. One conclusion is that increased precision is needed in sensing and controlling vehicle motions, a trend that can mimic that of the aerospace industry, and similarly benefit from increased use of redundant by-wire actuators

Computational driver behavior models for vehicle safety applications

The aim of this thesis is to investigate how human driving behaviors can be formally described in mathematical models intended for online personalization of advanced driver assistance systems (ADAS) or offline virtual safety evaluations. Both longitudinal (braking) and lateral (steering) behaviors in routine driving and emergencies are addressed. Special attention is paid to driver glance behavior in critical situations and the role of peripheral vision.First, a hybrid framework based on autoregressive models with exogenous input (ARX-models) is employed to predict and classify driver control in real time. Two models are suggested, one targeting steering behavior and the other longitudinal control behavior. Although the predictive performance is unsatisfactory, both models can distinguish between different driving styles.Moreover, a basic model for drivers\u27 brake initiation and modulation in critical longitudinal situations (specifically for rear-end conflicts) is constructed. The model is based on a conceptual framework of noisy evidence accumulation and predictive processing. Several model extensions related to gaze behavior are also proposed and successfully fitted to real-world crashes and near-crashes. The influence of gaze direction is further explored in a driving simulator study, showing glance response times to be independent of the glance\u27s visual eccentricity, while brake response times increase for larger gaze angles, as does the rate of missed target detections.Finally, the potential of a set of metrics to quantify subjectively perceived risk in lane departure situations to explain drivers\u27 recovery steering maneuvers was investigated. The most influential factors were the relative yaw angle and splay angle error at steering initiation. Surprisingly, it was observed that drivers often initiated the recovery steering maneuver while looking off-road.To sum up, the proposed models in this thesis facilitate the development of personalized ADASs and contribute to trustworthy virtual evaluations of current, future, and conceptual safety systems. The insights and ideas contribute to an enhanced, human-centric system development, verification, and validation process. In the long term, this will likely lead to improved vehicle safety and a reduced number of severe injuries and fatalities in traffic

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

IS THE MAGIC IN THE HANDS OR EYES? STUDYING THE EFFECTS OF DRIVER MONITORING STRATEGIES ON SITUATION AWARENESS, MIND WANDERING, AND CHANGE DETECTION BEHAVIOR IN LOW AND MEDIUM FIDELITY SEMI-AUTOMATED DRIVING ENVIRONMENTS

Advanced Driver Assist Systems (ADAS) are SAE level 2 features that require active driver control while engaged. Although drivers can have their feet off the pedals and hands off the steering wheel, they must maintain supervisory control of the vehicle. However, when these features are in use, drivers may become less aware of their surroundings, increasing the risk of accidents. To counter this problem, vehicle manufacturers use driver monitoring strategies to ensure drivers remain attentive while ADAS features are active. These monitoring strategies employ instructions to ensure drivers are engaged in the driving task. These instructions are broadly classified into hands-on-the-wheel and hands-free (eyes-on-the-road). The hands-on-the-wheel strategy measures driver engagement by examining steering wheel torque, while the hands-free strategy tracks the driver\u27s eyes to ensure they remain on the road. Although both strategies are commonly used in vehicles with SAE level 2 automation, there is a lack of publicly available data on their effectiveness and impact on takeover performance. In this dissertation, three studies were conducted to measure the effects of the hands-on-the-wheel and eyes-on-the-road driver monitoring strategies on situation awareness, change detection, mind-wandering, and gaze behavior. Study 1 was exploratory and utilized a low-fidelity semi-automated driving task to examine the effects of the two engagement strategies on driver attention during level 2 ADAS driving. Study 2 was an extension of Study 1 and moved to more naturalistic automation-related change detection in addition to a SAGAT freeze-probe protocol and comfort, fatigue, engagement, and takeover readiness measures in addition to the ones measured in Study 1. Study 3 extended Study 2 in a medium-fidelity driving simulator to investigate the effects of the two driver engagement strategies on driving performance variables and driver attention. Study 1 found that the hands-on-the-wheel strategy promoted less mind wandering during level 2 automated driving. Study 2 found that while the hands-on-the-wheel strategy also promoted less mind wandering, it promoted higher situation awareness, more perceived engagement with automated driving, less self-reported fatigue, and faster response to takeover requests. On the contrary, Study 3 found that the eyes-on-the-road strategy exhibited higher SA, faster responses to takeover requests, and less steering wheel variability but closer following distances post-takeover. Although the three studies have mixed findings, the hands-on-the-wheel strategy appears more promising because it engages drivers physically with the driving task, potentially leading to safer driving behaviors. This work has broader implications for SAE level 2 and 3 ADAS features, reinforcing the need for an engagement strategy with driver monitoring systems. Even as level 3 and higher technologies are developed, the results here inform strategies for automation-level step-downs as the drivers are brought back into actively controlling the vehicle

Statistical‐based approach for driving style recognition using Bayesian probability with kernel density estimation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166283/1/itr2bf00581.pd

Investigating the transition from normal driving to safety-critical scenarios

Investigation of the correlation between factors associated with crash development has enabled the implementation of methods aiming to avert and control crash causation at various points within the crash sequence (Evans, 2006). Partitioning the crash sequence is important because intricated crash causation sequences can be deconstructed and effective prevention strategies can be suggested (Wu & Thor, 2015). Towards this purpose, Tingvall et al. (2009) documented the so-called integrated safety chain which described the change of crash risk on the basis of a developing sequence of events that led to a collision. This thesis examines the crash sequence development and thus, the transition from normal driving to safety critical scenarios. [Continues.

Exploring perceptions of Advanced Driver Assistance Systems (ADAS) in older drivers with age-related declines

Perceptions of Advanced Driver Assistance Systems (ADAS) were explored in two semi-structured face-to-face focus group studies of 42 older drivers (aged 65 years and older) with and without age-related declines. Study 1 explored perceptions regarding ADAS, focusing on visual, auditory, physical, and cognitive factors. Study 2 extended this by additionally exploring perceptions following exposure to videos and stationary vehicle demonstrations of an ADAS. Participants had a range of visual, hearing, memory, and health characteristics which impacted on their daily life. In both studies, some participants had insights regarding various ADAS technologies prior to the study, but many were unfamiliar with these systems. Nevertheless, overall, participants reported that ADAS would assist them to drive as they age and increase their mobility and independence. There were comments regarding the benefits of warning alerts, although the potential for them to be distracting was also highlighted. Participants with vision impairment preferred audio alerts and participants with hearing impairment preferred visual display alerts. Findings highlighted the potential for ADAS to assist those with age-related declines and the need to increase the flexibility of warning system alerts to suit the varying requirements of older drivers, as well as to reduce the complexity of vehicle interfaces. Collectively, these strategies would maximize the benefits of these vehicles to increase the mobility, independence, and quality of life of older drivers with and without age-related declines