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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 drivers' SA maintenance and readiness to drive following high automation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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., Competing Interests: The authors declare no conflict of interest.