Automated-Manual Transitions: Human Capabilities and Adaptive Cruise Control (original) (raw)
2017
This report presents human factors research to examine the effects of cooperative adaptive cruise control (CACC) on driver performance in a variety of situations. It summarizes driving simulator experiments in which the driver was required to drive in a stream of vehicles. Participants experienced a vehicle merge in front of them as well as an emergency event that required driver intervention. The participants' preferred following time gap did not significantly affect collision avoidance. However, those participants following at shorter distances were more likely to intervene more rapidly that those following at a far distance. These findings support the idea that performance depends more on overall CACC following distance settings than with drivers' personal preferences. This will allow CACC systems to implement a single following distance gap (or set of gaps based on vehicle physics). The results show that it is critical that drivers receive clear alerts when it is necessary to take over control of the vehicle. Without such measures, it is possible that CACC implementation may not result in improved roadway safety. This report should be useful to transportation professionals, State transportation departments, and researchers interested in the effects of automation on driver behavior and performance.
Theoretical Issues in Ergonomics Science, 2019
Since returning to academia and gaining his PhD in 2013, David has been involved in a broad range of industry, UK and EU funded projects primarily concerning the design, evaluation and acceptance of novel and emerging in-vehicle interfaces and systems for both road and rail transport, and the development of experimental techniques to support this work. Professor Gary Burnett holds a chair in Transport Human Factors within the Faculty of Engineering at the University of Nottingham. He has over 25 years' experience in Human Factors research and development relating to advanced technology within road-based vehicles. His work addresses key safety, usability, and acceptance issues for advanced in-car systems and Human-Machine Interfaces (HMIs), and he is particularly concerned with the assessment of driver distraction. Rebecca Matthias works at Jaguar Land Rover in the Research Department. As a Human Factors Group Leader in Capability Research, Rebecca's work focuses on the human factors aspects of automated driving, in particularly occupant interaction with the highly automated vehicle and vehicle behaviour. Simon Thompson works at Jaguar Land Rover in the Research Department. As a Human Machine Interface Specialist, Simon focuses on technical aspects such as engineering, human factors, and psychological factors. Simon is responsible for research projects and activities including: designing system-driver interfaces; providing HMI and ergonomics guidance to engineers; competitor benchmarking and assessment; designing, organising and conducting user trials; reviewing and creating internal HMI standards; and developing technology and feature HMI strategies. Lee Skrypchuk works at Jaguar Land Rover in the Research Department. As a Human Machine Interface Technical Specialist, Lee focuses on technical aspects such as engineering, human factors, and psychological factors. Lee has led projects including head-up display, driver monitoring, and gesture control, and holds responsibility for research roadmaps in this area. Getting the Driver back into the Loop: The Quality of manual Vehicle Control following long and short non-critical Transfer-of-control Requests: TI:NS Specific vehicle automation use-cases such as traffic jams will be the first level 3 functions on the market. When the 'traffic jam pilot' nears its limits in noncritical situations, control needs to be handed back to the driver, enabling appropriate situation awareness (SA) and vehicle handling. According to previous research, operational vehicle stabilisation can be achieved within a transfer-of-control (TOC) of a few seconds in simple traffic environments, but tactical level decisions benefit from longer handover times. To date, the effects of non-critical TOCs have not been studied using set time frames. To investigate the impact of short (unplanned, 5 seconds) and long (planned, 50 seconds) TOC requests, while playing/not playing an engaging tablet game, a simulator experiment was conducted with 16 participants. Comparisons of the 60-secondperiod of manual driving following automation suggest better longitudinal vehicle control as well as more appropriate SA following the long TOC request, and automation periods without the game. However, following no engaging game, lateral performance was worse during the first 10 seconds of manual driving. Control-level visual search patterns did not change with TOC time or the game. Future research needs to consider support for drivers' SA maintenance and readiness to drive following high automation.
Behavioral adaptation to adaptive cruise control
PsycEXTRA Dataset
This test-track study assessed whether adaptive cruise control (ACC) induces behavioural adaptation in drivers. Eighteen experienced drivers drove a test vehicle while following a lead vehicle in three counterbalanced conditions: No ACC (self-maintained average headway of 2 s), ACC-Short (headway of 1.4 s) and ACC-Long (headway of 2.4 s). Results demonstrate that ACC can induce behavioural adaptation in drivers in potentially safety-critical ways. Compared to driving unsupported, participants located significantly more items per minute on a secondary task when using ACC, while their response times to a hazard detection task increased. This effect was particularly pronounced in those scoring high on a sensation-seeking scale. Using ACC resulted in significantly more lane position variability, an effect that was also more pronounced in high sensation-seekers. DriversÕ trust in ACC increased significantly after using the system, and these ratings did not change despite a simulated failure of the ACC system during the ACC-Long condition. Response time to the simulated ACC failure was related to a driverÕs locus of control: Externals intervened more slowly than Internals. All drivers reported relying on the ACC system to keep their vehicle at a safe distance from the lead vehicle. Results are consistent with similar research conducted on lane departure warning systems. Driver awareness training is a potential preventive strategy that could minimize the behavioural adaptation associated with novel in-vehicle systems such as ACC.
Behavioural adaptation to adaptive cruise control (ACC): implications for preventive strategies
Transportation Research Part F: Traffic Psychology and Behaviour, 2004
This test-track study assessed whether adaptive cruise control (ACC) induces behavioural adaptation in drivers. Eighteen experienced drivers drove a test vehicle while following a lead vehicle in three counterbalanced conditions: No ACC (self-maintained average headway of 2 s), ACC-Short (headway of 1.4 s) and ACC-Long (headway of 2.4 s). Results demonstrate that ACC can induce behavioural adaptation in drivers in potentially safety-critical ways. Compared to driving unsupported, participants located significantly more items per minute on a secondary task when using ACC, while their response times to a hazard detection task increased. This effect was particularly pronounced in those scoring high on a sensation-seeking scale. Using ACC resulted in significantly more lane position variability, an effect that was also more pronounced in high sensation-seekers. DriversÕ trust in ACC increased significantly after using the system, and these ratings did not change despite a simulated failure of the ACC system during the ACC-Long condition. Response time to the simulated ACC failure was related to a driverÕs locus of control: Externals intervened more slowly than Internals. All drivers reported relying on the ACC system to keep their vehicle at a safe distance from the lead vehicle. Results are consistent with similar research conducted on lane departure warning systems. Driver awareness training is a potential preventive strategy that could minimize the behavioural adaptation associated with novel in-vehicle systems such as ACC.
Transition to manual: Driver behaviour when resuming control from a highly automated vehicle
Transportation Research Part F: Traffic Psychology and Behaviour, 2014
A driving simulator study was designed to investigate drivers' ability to resume control from a highly automated vehicle in two conditions: (i) when automation was switched off and manual control was required at a system-based, regular interval and (ii) when transition to manual was based on the length of time drivers were looking away from the road ahead. In addition to studying the time it took drivers to successfully resume control from the automated system, eye tracking data were used to observe visual attention to the surrounding environment and the pattern of drivers' eye fixations as manual control was resumed in the two conditions. Results showed that drivers' pattern of eye movement fixations remained variable for some time after automation was switched off, if disengagement was actually based on drivers' distractions away from the road ahead. When disengagement was more predictable and system-based, drivers' attention towards the road centre was higher and more stable. Following a lag of around 10 s, drivers' lateral control of driving and steering corrections (as measured by SDLP and high frequency component of steering, respectively) were more stable when transition to manual control was predictable and based on a fixed time. Whether automation transition to manual was based on a fixed or variable interval, it took drivers around 35-40 s to stabilise their lateral control of the vehicle. The results of this study indicate that if drivers are out of the loop due to control of the vehicle in a limited self-driving situation (Level 3 automation), their ability to regain control of the vehicle is better if they are expecting automation to be switched off. As regular disengagement of automation is not a particularly practical method for keeping drivers in the loop, future research should consider how to best inform drivers of their obligation to resume control of driving from an automated system.
Situation Awareness and Transitions in Highly Automated Driving: A Framework and Mini Review
Journal of Ergonomics
The human factors of transition in highly automated driving is becoming crucial, because transitions between the driver and the automation will remain a key element of automated driving until fully automated driving replace manual operation. This paper aims to suggest the framework to investigate human factors in transition of highly automated driving. The framework classifies transitions according to the transition initiator, control after transition, and situation awareness. Based on the framework, we retrieved previous studies and categorized their experimental designs. The inclusion criteria for the review were transitions which involved SAE level 3 automated driving and the research topics on driver behavior and/or performance during a transition. Finally, we interpreted the empirical studies on transitions using the proposed framework, and suggested areas for future research.
Drivers' Adaptation to Adaptive Cruise Control: Examination of Automatic and Manual Braking
IEEE Transactions on Intelligent Transportation Systems, 2000
Drivers may adapt to the automatic braking control feature available on adaptive cruise control (ACC) in ways unintended by designers. This study examines drivers' adaptation using a conceptual model of adaptive behavior developed and examined quantitatively using logistic regression techniques. Data for this model come from a field operational test on the use of an advanced collision avoidance system, which integrated forward collision warning and ACC functions. A sample of "closing" events was extracted from a subset of these ACC data. The logistic regression model predicted the drivers' likelihood to intervene (i.e., manually brake) whenever ACC began braking or slowing down the vehicle. The results indicate that several factors influence drivers' response, including the environment, selected gap setting, speed, and drivers' age. Safety consequences and the design of future ACC systems based on drivers' adaptation to these factors are discussed.
Behavioural effects of Advanced Cruise Control Use
2020
In this study, a meta-analytic approach was used to analyse effects of Advanced Cruise Control (ACC) on driving behaviour reported in seven driving simulator studies. The effects of ACC on three consistent outcome measures, namely, driving speed, headway and driver workload have been analysed. The indicators of speed, headway and workload have been chosen because they are assumed to be directly affected by the ACC support, their relationship with road safety is reasonably established and they are the most frequently used outcome measures in the sample of analysed studies. The results suggest that different operational settings of ACC that are important for the level of support provided by the system, are significant for the effects ACC have on various aspects of driving behaviour, i.e. on mean driving speed and mean time headway. The obtained effect sizes clustered in two groups, with more intervening ACCs having the effects of an increased driving speed and decreased mean time head...
Field Evaluation of Safety Impacts of Adaptive Cruise Control
ITS Journal - Intelligent Transportation Systems Journal, 2001
This paper provides an attempt at evaluating the safety impacts of an Adaptive Cruise Control (ACC) system relative to Conventional Cruise Control (CCC) utilizing data that were gathered as part of a Field Operational Test (FOT) in Ann Arbor, Michigan. The safety of the ACC system is quantified considering three surrogate safety measures. The first safety measure considers the car-following behavior of an ACC system relative to manual driving in order to identify potential differences in driver/vehicle aggressiveness. The second safety measure considers changes in demands on driver resources associated with ACC technology. The third, and final safety measure, considers differences in number of braking maneuvers and near encounters associated with ACC and CCC driving. These three surrogate safety measures are utilized to identify any potential hazards that could be associated with an ACC system.