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.

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.

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.

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.

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.

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.