Abstract
In highly automated driving, the driver can engage in a nondriving task but sometimes has to take over control. We argue that current takeover quality measures, such as the maximum longitudinal acceleration, are insufficient because they ignore the criticality of the scenario. This paper proposes a novel method of quantifying how well the driver executed an automation-to-manual takeover by comparing human behaviour to optimised behaviour as computed using a trajectory planner. A human-in-the-loop study was carried out in a high-fidelity 6-DOF driving simulator with 25 participants. The takeover required a lane change to avoid roadworks on the ego-lane while taking other traffic into consideration. Each participant encountered six different takeover scenarios, with a different time budget (5 s, 7 s, or 20 s) and traffic density level (low or medium). Results showed that drivers exhibited a considerably higher longitudinal and lateral acceleration than the optimised behaviour, especially in the short time budget scenarios. In scenarios of medium traffic density, the trajectory planner showed a moderate deceleration to let a vehicle in the left lane pass; many participants, on the other hand, did not decelerate before making a lane change, resulting in a dangerous emergency brake of the left-lane vehicle. In conclusion, our results illustrate the value of assessing human takeover behaviour relative to optimised behaviour. Using the trajectory planner, we showed that human drivers are unable to behave optimally in urgent scenarios and that, in some conditions, a medium deceleration, as opposed to a maximal or minimal deceleration, is optimal.
Highlights
Over the past decades, improvements in sensor technology, artificial intelligence, and control systems have led to an increase in vehicle automation
, the results indicate that the differences between a human takeover manoeuvre and that of the trajectory planner are large in time-critical scenarios. at is, humans seem unable to behave optimally when temporal demands are high
This study showed benefits of assessing takeover quality in comparison with a reference behaviour
Summary
Improvements in sensor technology, artificial intelligence, and control systems have led to an increase in vehicle automation. Adaptive cruise control (ACC), a technology that was introduced in the 1990s, has the potential to increase driver comfort by automating the longitudinal control task [1,2,3,4]. E recent arrival of assistance systems that can perform lateral control has led to a situation where the driver is provided with the option to detach him- or herself from the control loop of the vehicle [5, 6]. Radlmayr et al [11] used the maximum longitudinal acceleration as a takeover quality measure, whereas Zeeb et al [12] used the maximum lateral acceleration and centerline deviation. Takeover quality is quantified using the minimum time to collision (MTTC) [13,14,15,16]
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