Paving the Roadway for Safety of Automated Vehicles: An Empirical Study on Testing Challenges

The technology in the area of automated vehicles is gaining speed and promises many advantages. However, with the recent introduction of conditionally automated driving, we have also seen accidents. Test protocols for both, conditionally automated (e.g., on highways) and automated vehicles do not exist yet and leave researchers and practitioners with different challenges. For instance, current test procedures do not suffice for fully automated vehicles, which are supposed to be completely in charge for the driving task and have no driver as a back up. This paper presents current challenges of testing the functionality and safety of automated vehicles derived from conducting focus groups and interviews with 26 participants from five countries having a background related to testing automotive safety-related topics.We provide an overview of the state-of-practice of testing active safety features as well as challenges that needs to be addressed in the future to ensure safety for automated vehicles. The major challenges identified through the interviews and focus groups, enriched by literature on this topic are related to 1) virtual testing and simulation, 2) safety, reliability, and quality, 3) sensors and sensor models, 4) required scenario complexity and amount of test cases, and 5) handover of responsibility between the driver and the vehicle.Comment: 8 page

Safe and seamless transfer of control authority - exploring haptic shared control during handovers

This research aimed at investigating the impact of lateral assistance systems on drivers' performance and behaviour during transitions from Highly Automated Driving (HAD). The thesis focused on non-critical transitions and analysed the differences between system and user-initiated transitions. Hence, two experiments were developed and conducted in driving simulators to address questions relating to how handover procedures, which provide varying levels of lateral assistance, affect drivers' performance and behaviour at different stages of the transition. In particular, it was investigated which type of assistance yields better results depending on who initiated the transition of control. Drivers were induced to be Out-Of-The-Loop (OOTL) during periods of HAD and then exposed to both system and user-initiated transitions. Results showed that after user-initiated transitions, drivers were generally more engaged with the steering task and the provided assistance was not helpful and, in some cases, caused steering conflicts and a comfort drop. On the contrary, after system-initiated transitions, drivers were not engaged with the steering control and were more prone to gaze wandering. Strong lateral assistance proved to be most beneficial within the first 5 seconds of the transition, when drivers were not committed to the steering control. The provision of assistance at an operational level, namely when drivers had to keep the lane centre, was not enough to ensure good performance at a tactical level. Drivers were able to cope with tactical tasks, presented as lane changes, only after around 10 seconds from the start of the transitions in both user and system initiated cases (Chapter 3 and Chapter 4). The introduction of non-continuous lateral assistance, used to trigger steering conflicts and, in turn, a faster steering engagement, did not yield particular benefits during user-initiated transitions but it might have triggered a faster re-engagement process in system-initiated ones (Chapter 5). The results suggest that assisting drivers after user-initiated transitions is not advisable as the assistance might induce steering conflicts. On the contrary, it is extremely beneficial to assist drivers during system-initiated transitions because of their low engagement with the driving task. The thesis concludes with a general overview of the conducted studies and a discussion on future studies to take this research forward

Trusted autonomous vehicles: an interactive exhibit

Recent surveys about autonomous vehicles show that the public is concerned about the safety consequences of system or equipment failures and the vehicles' reactions to unexpected situations. We believe that informing about the technology and quality, e.g., safety and reliability, of autonomous vehicles is paramount to improving public expectations, perception and acceptance. In this paper, we report on the design of an interactive exhibit to illustrate (1) basic technologies employed in autonomous vehicles, i.e., sensors and object classification; and (2) basic principles for ensuring their quality, i.e., employing software testing and simulations. We subsequently report on a public engagement event involving this exhibit at the Royal Society Summer Science Exhibition 2019 in the exhibit titled "Trusted Autonomous Vehicles". We describe the process of designing and developing the artefacts used in our exhibit, the theoretical background associated to them, the design of our stand, and the lessons learned. The activities and findings of this study can be used by other educators and researchers interested in promoting trust in autonomous vehicles among the general public

Effect of cognitive load on drivers’ State and task performance during automated driving: Introducing a novel method for determining stabilisation time following take-over of control

This research paper explores the impact of cognitive load on drivers’ physiological state and driving performance during an automated driving to manual control transition scenario, using a driving simulator. Whilst driving in the automated mode, cognitive load was manipulated using the “N-Back” task, which participants engaged with via a visual display. Results suggest that non-optimal levels of workload during the automated driving conditions impair driving performance, especially lateral control of the vehicle, and the magnitude of this impairment varied with increasing cognitive load. In addition to these findings, the present paper introduces a novel method for determining stabilisation times of both driver state and driving performance indicators following a transition of vehicle control. Using this method we demonstrate that mean and standard deviation of lane position impairments were found to take longer to stabilise following transition to manual driving following a higher level of cognitive load during the automated driving period, taking up to 22 s for driving performance to normalise after take-over. In addition, heart rate parameters take between 20 and 30 s to stabilise following a planned take-over request. Finally, this paper demonstrates how the magnitude of cognitive load can be estimated in context of automated driving using physiological measures, captured by consumer electronic devices. We discuss the impact our findings have on the design of SAE Level 3 systems. Relevant suggestions are provided to the research community and automakers working on future implementation of vehicles capable of conditional automation

Semi-autonomous vehicles as a cognitive assistive device for older adults

Losing the capacity to drive due to age-related cognitive decline can have a detrimental impact on the daily life functioning of older adults living alone and in remote areas. Semi-autonomous vehicles (SAVs) could have the potential to preserve driving independence of this population with high health needs. This paper explores if SAVs could be used as a cognitive assistive device for older aging drivers with cognitive challenges. We illustrate the impact of age-related changes of cognitive functions on driving capacity. Furthermore, following an overview on the current state of SAVs, we propose a model for connecting cognitive health needs of older drivers to SAVs. The model demonstrates the connections between cognitive changes experienced by aging drivers, their impact on actual driving, car sensors' features, and vehicle automation. Finally, we present challenges that should be considered when using the constantly changing smart vehicle technology, adapting it to aging drivers and vice versa. This paper sheds light on age-related cognitive characteristics that should be considered when developing future SAVs manufacturing policies which may potentially help decrease the impact of cognitive change on older adult drivers