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 non-critical 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 hand-over 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-second-period 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 driver's SA maintenance and readiness to drive following high automation.

In order to design an advanced human-automation collaboration system for highly automated vehicles, research into the driver's neuromuscular dynamics is needed. In this paper, a dynamic model of drivers' neuromuscular interaction with a steering wheel is first established. The transfer function and the natural frequency of the systems are analyzed. In order to identify the key parameters of the driver-steering-wheel interacting system and investigate the system properties under different situations, experiments with driver-in-the-loop are carried out. For each test subject, two steering tasks, namely the passive and active steering tasks, are instructed to be completed. Furthermore, during the experiments, subjects manipulated the steering wheel with two distinct postures and three different hand positions. Based on the experimental results, key parameters of the transfer function model are identified by using the Gauss-Newton algorithm. Based on the estimated model with identified parameters, investigation of system properties is then carried out. The characteristics of the driver neuromuscular system are discussed and compared with respect to different steering tasks, hand positions, and driver postures. These experimental results with identified system properties provide a good foundation for the development of a haptic take-over control system for automated vehicles.

Autonomous driving presents an exciting new development in vehicle technology. It poses a new challenge in driver-automation collaboration particularly during handover transitions between human and machine. In order to deal with this problem, this paper proposes a novel control framework for the haptic take-over system. The high-level framework of the haptic take-over control system, which takes driver cognitive workload, neuromuscular dynamics and optimal trajectory planning into consideration, is developed. Under the proposed framework, the determination approach of the optimal input sequence is introduced. The model of the allowed driver take-over authority, which is associated with driver's cognitive workload, as well as muscle readiness during takeover, is investigated and developed. The haptic feedback torque controller is then designed so as to minimize the deviation between the allowed control authority and driver's current degree of participation. A handover process, along with the proposed take-over control method, is also simulated. The simulation results validate the feasibility and effectiveness of the proposed approach.

Although at present legislation does not allow drivers in a Level 3 autonomous vehicle to engage in a secondary task, there may become a time when it does. Monitoring the behaviour of drivers engaging in various non-driving activities (NDAs) is crucial to decide how well the driver will be able to take over control of the vehicle. One limitation of the commonly used face-based head tracking system, using cameras, is that sufficient features of the face must be visible, which limits the detectable angle of head movement and thereby measurable NDAs, unless multiple cameras are used. This paper proposes a novel orientation sensor based head tracking system that includes twin devices, one of which measures the movement of the vehicle while the other measures the absolute movement of the head. Measurement error in the shaking and nodding axes were less than 0.4°, while error in the rolling axis was less than 2°. Comparison with a camera-based system, through in-house tests and on-road tests, showed that the main advantage of the proposed system is the ability to detect angles larger than 20° in the shaking and nodding axes. Finally, a case study demonstrated that the measurement of the shaking and nodding angles, produced from the proposed system, can effectively characterise the drivers’ behaviour while engaged in the NDAs of chatting to a passenger and playing on a smartphone.

In order to develop an advanced haptic take-over system for highly automated vehicles, research into the driver's neuromuscular dynamics is needed. In this paper a dynamic model of drivers' neuromuscular interaction with a steering wheel is firstly established. The transfer function and the natural frequency of the systems are analysed. In order to identify the key parameters of the driver-steering-wheel coupled system and investigate the system properties under different situations, experiments with drive-in-the-loop are carried out. For each test subject, two steering tasks, namely the passive and active steering tasks, are instructed to be completed. Furthermore, during the experiments, subjects manipulated the steering wheel with two distinct postures and three different hand positions. Based on the test results, key parameters of the transfer function and system properties are identified and investigated. The data and characteristics of the driver neuromuscular system are discussed and compared with respect to different steering tasks, hand positions and driver postures. These test results identified system properties that provide a good foundation for the development of a haptic take-over control system for automated vehicles.

Six experienced drivers each undertook five 30-min journeys (portrayed as ‘daily commutes’ i.e. one on each of five consecutive weekdays) in a medium-fidelity driving-simulator engineered to mimic a highly-automated vehicle. Participants were encouraged to act as they might in such a vehicle by bringing with them their own objects/devices to use. During periods of automation, participants were quickly engrossed by their chosen activities, many of which had strong visual, manual and cognitive elements, and required postural adaptation (e.g. moving/reclining the driver’s seat); the steering wheel was typically used to support objects/devices. Consistently high subjective ratings of trust suggest that drivers were unperturbed by the novelty of highly-automated driving and generally willing to allow the vehicle to assume control; ratings of situational awareness varied considerably indicating mixed opinions. Qualitative results are discussed in the context of the re-design of vehicles to enable safe and comfortable engagement with secondary activities during high-automation.

Highly-automated vehicles will provide the freedom for drivers to engage in secondary activities while the vehicle is in control. However, little is known regarding the nature of activities that drivers will undertake, and how these may impact drivers’ ability to resume manual control. In a novel, long-term, qualitative simulator study, six experienced drivers completed the same 30-minute motorway journey (portrayed as their commute to work) at the same time on five consecutive weekdays in a highly-automated car; a system ‘health-bar’ indicated the overall status of the automated system during each drive. Participants were invited to bring with them any objects or devices that they would expect to use in their own (automated) vehicle during such a journey, and use these freely during the drives. Inclement weather (heavy fog) on the penultimate day of testing presented an unexpected, emergency 5.0-second take-over request (indicated by an urgent auditory alarm and a flashing visualicon replacing a system ‘health-bar’). Thematic video analysis shows that participants were quickly absorbed by a variety of secondary activities/devices, which typically demanded high levels of visual, manual and cognitive attention, and postural adaptation (e.g. moving/reclining the driver’s seat). The steering wheel was routinely used as a support for objects/devices. Drivers were required to rapidly discharge secondary devices/activities and reestablish driving position/posture following the unexpected, emergency hand-over request on day four. This resulted in notable changes in participants’ subjective ratings of trust on the final day of testing, with someparticipants apparently more sceptical of the system following the emergency hand-over event, whereas others were more trusting than before. Qualitative results are presented and discussed in the context of the re-design of vehicles to enable the safe and comfortable execution of secondary activities during high-automation, whileenabling effective transfer of control.