Drivers' Performance in Non-critical Take-Overs From an Automated Driving System-An On-Road Study.

OBJECTIVE: The objective of this semi-controlled study was to investigate drivers' performance when resuming control from an Automated Driving System (ADS), simulated through the Wizard of Oz method, in real traffic. BACKGROUND: Research on take-overs has primarily focused on urgent scenarios. This article aims to shift the focus to non-critical take-overs from a system operating in congested traffic situations. METHOD: Twenty drivers drove a selected route in rush-hour traffic in the San Francisco Bay Area, CA, USA. During the drive, the ADS became available when predetermined availability conditions were fulfilled. When the system was active, the drivers were free to engage in non-driving related activities. RESULTS: The results show that drivers' transition time goes down with exposure, making it reasonable to assume that some experience is required to regain control with comfort and ease. The novel analysis of after-effects of automated driving on manual driving performance implies that the after-effects were close to negligible. Observational data indicate that, with exposure, a majority of the participants started to engage in non-driving related activities to some extent, but it is unclear how the activities influenced the take-over performance. CONCLUSION: The results indicate that drivers need repeated exposure to take-overs to be able to fully resume manual control with ease. APPLICATION: Take-over signals (e.g., visuals, sounds, and haptics) should be carefully designed to avoid startle effects and the human-machine interface should provide clear guidance on the required take-over actions.

Festschrift in honour of Professor Neville Stanton: A lone crusader in a world of driving simulators.

The automotive future has always pointed to a world of intelligent co-pilots and robot cars, but perhaps no more so than Knight Rider. In this 1980's television series the fictional Knight Industries Two Thousand (KITT) was a supercomputer on wheels with 1000 megabytes of memory. The protagonist was Michael Knight, a young loner on a crusade to champion the cause of the innocent and the helpless. This was a shadowy flight into the trials and tribulations of different levels of automation, re-claiming control when automation failed, and a wilful, chatty computer co-driver. An amusing metaphor, perhaps, for the research impact made by Neville Stanton in the field of vehicle automation. Without question - to paraphrase the Knight Rider outro - "one man can make a difference". This festschrift in Neville's honour tells the story of how.

A toolbox for automated driving on the STISIM driving simulator.

Driving simulators have been used since the beginning of the 1930s to assist researchers in assessing driver behaviour without putting the driver in harm's way. The current manuscript describes the implementation of a toolbox for automated driving research on the widely used STISIM platform. The toolbox presented in this manuscript allows researchers to conduct flexible research into automated driving, enabling independent use of longitudinal control, and a combination of longitudinal and lateral control, and is available as an open source download through GitHub. The toolbox allows the driver to adjust parameters such as set speed (in 5 kph increments) and time-headway (in steps of 1, 1.5, and 2 s) as well as automation mode dynamically, while logging additional variabless that STISIM does not provide out-of-the-box (time-headway, time to collision). Moreover, the toolbox presented in this manuscript has gone through validation trials showing accurate speed, time-headway, and lane tracking, as well as transitions of control between manual and automated driving. •A toolbox was developed for STISIM driving simulators.•The toolbox allows for automated driving.•Functionality includes tracking of speed, headway, and lane.

Is partially automated driving a bad idea? Observations from an on-road study.

The automation of longitudinal and lateral control has enabled drivers to become "hands and feet free" but they are required to remain in an active monitoring state with a requirement to resume manual control if required. This represents the single largest allocation of system function problem with vehicle automation as the literature suggests that humans are notoriously inefficient at completing prolonged monitoring tasks. To further explore whether partially automated driving solutions can appropriately support the driver in completing their new monitoring role, video observations were collected as part of an on-road study using a Tesla Model S being operated in Autopilot mode. A thematic analysis of video data suggests that drivers are not being properly supported in adhering to their new monitoring responsibilities and instead demonstrate behaviour indicative of complacency and over-trust. These attributes may encourage drivers to take more risks whilst out on the road.

Driving Performance After Self-Regulated Control Transitions in Highly Automated Vehicles.

OBJECTIVE: This study aims to explore whether driver-paced, noncritical transitions of control may counteract some of the aftereffects observed in the contemporary literature, resulting in higher levels of vehicle control. BACKGROUND: Research into control transitions in highly automated driving has focused on urgent scenarios where drivers are given a relatively short time span to respond to a request to resume manual control, resulting in seemingly scrambled control when manual control is resumed. METHOD: Twenty-six drivers drove two scenarios with an automated driving feature activated. Drivers were asked to read a newspaper or monitor the system and relinquish or resume control from the automation when prompted by vehicle systems. Driving performance in terms of lane positioning and steering behavior was assessed for 20 seconds post resuming control to capture the resulting level of control. RESULTS: It was found that lane positioning was virtually unaffected for the duration of the 20-second time span in both automated conditions compared to the manual baseline when drivers resumed manual control; however, significant increases in the standard deviation of steering input were found for both automated conditions compared to baseline. No significant differences were found between the two automated conditions. CONCLUSION: The results indicate that when drivers self-paced the transfer back to manual control they exhibit less of the detrimental effects observed in system-paced conditions. APPLICATION: It was shown that self-paced transitions could reduce the risk of accidents near the edge of the operational design domain. Vehicle manufacturers must consider these benefits when designing contemporary systems.

Takeover Time in Highly Automated Vehicles: Noncritical Transitions to and From Manual Control.

OBJECTIVE: The aim of this study was to review existing research into driver control transitions and to determine the time it takes drivers to resume control from a highly automated vehicle in noncritical scenarios. BACKGROUND: Contemporary research has moved from an inclusive design approach to adhering only to mean/median values when designing control transitions in automated driving. Research into control transitions in highly automated driving has focused on urgent scenarios where drivers are given a relatively short time span to respond to a request to resume manual control. We found a paucity in research into more frequent scenarios for control transitions, such as planned exits from highway systems. METHOD: Twenty-six drivers drove two scenarios with an automated driving feature activated. Drivers were asked to read a newspaper, or to monitor the system, and to relinquish, or resume, control from the automation when prompted by vehicle systems. RESULTS: Significantly longer control transition times were found between driving with and without secondary tasks. Control transition times were substantially longer than those reported in the peer-reviewed literature. CONCLUSION: We found that drivers take longer to resume control when under no time pressure compared with that reported in the literature. Moreover, we found that drivers occupied by a secondary task exhibit larger variance and slower responses to requests to resume control. Workload scores implied optimal workload. APPLICATION: Intra- and interindividual differences need to be accommodated by vehicle manufacturers and policy makers alike to ensure inclusive design of contemporary systems and safety during control transitions.