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Regaining Situational Awareness as a
User in Charge: Responding to transition
demands in automated vehicles
January 2025
Department for Transport
Great Minster House
33 Horseferry Road
London
SW1P 4DR
enquiries@ccav.gov.uk

Rega
ining Situational Awareness as a User in Charge: Responding to transition demands in
automated vehicles
2
Control sheet
Project Manager Dr Clare Mutzenich
Quality Assurance Hannah Hughes 10
th
September 2024
Version Control
Draft 1 31
st
July 2024
Draft 2 27
th
August 2024
Final report 11
th
September 2024
Date of Approval Stephen Devlin
7
th
January 2025
This report has been prepared by Lacuna Agency, a strategic insights agency serving
clients in public sector, automotive, luxury, and sport. We combine scientific techniques
with evidence-based practices to design innovative research for hard-to-reach or difficult-
to-study audiences. Our work also extends to the broader mobility and transport sectors,
where we assist governments, manufacturers and network providers in planning for future
needs.

Re
gaining Situational Awareness as a User in Charge: Responding to transition demands in
automated vehicles
3
Executive Summary
Background to the Project
In 2022, the Law Commission of England and Wales and the Scottish Law Commission (The
Law Commissions) published a report suggesting a substantial overhaul of the legal
framework governing automated vehicles. This joint report conducted a comprehensive
review of the regulatory structure for automated vehicles on public roads and highways and
introduced a novel legal entity known as the User-in-Charge (UiC), an individual situated
within the vehicle and capable of operating the driving controls while a self-driving feature is
engaged but not a driver (Law Commissions, 2022a). The UiC does not have to monitor the
road while the self-driving system is active.
This project explores the transition from self-driving technology to human-operated driving,
particularly focusing on the role of the UiC, who must be ready to take control when the
automated system issues a takeover request. A takeover request occurs when the
automated driving system encounters emergencies or conditions outside its programming.
The UiC receives the takeover request via visual, auditory, or haptic cues.
The first approved system under these guidelines is the Automated Lane Keeping System
(ALKS), which requires the UiC to take over within 10 seconds of a takeover request. As
self-driving technologies become more prevalent, questions arise regarding the
permissibility of engaging in Non-Driving Related Activities (NDRA) while using vehicles with
activated self-driving features such as ALKS. There is potential for certain NDRAs to be
allowed, provided they do not compromise the driver's ability to resume control of the vehicle
safely when a takeover request is issued by the system.
Upon receiving a takeover request, the UiC must suddenly focus on building Situational
Awareness (SA) from the surrounding road environment to enable a safe takeover. SA is
critical for safe driving, including during the transition from automated to manual control. SA
involves three levels: perception of the environment, comprehension of the situation, and
projection of future events. The time required to gain sufficient SA after a takeover request
is crucial, with studies showing response times ranging from 3 to 20 seconds. While simple
tasks, like basic perceptual awareness, are processed quickly, more complex activities that
involve higher levels of situational awareness—such as interpreting road signs or
anticipating how current events will impact the road ahead—require more time.
Project Aims
This project focused on exploring the implications of NDRAs in vehicles with self-driving
capabilities, when a UiC must respond to a transition demand. As the use of ALKS becomes
more prevalent, it is essential to understand which NDRAs can be performed (within the 10
seconds mandated by ALKS regulations) without compromising the ability to safely resume
control of the vehicle.
The project had the following objectives:

Rega
ining Situational Awareness as a User in Charge: Responding to transition demands in
automated vehicles
4
• Investigate which non-driving related activities (NDRAs), if any, can be safely
performed during non-driving periods in cars with self-driving features but requiring a
UiC.
• Establish mechanisms for measuring SA and determine appropriate thresholds to
ensure safe takeover and resumption of manual driving.
• Understand potential variations in the impact of NDRAs across different scenarios to
inform policy development.
To achieve these objectives, Lacuna Agency worked in partnership with University
College London (UCL) and Loughborough University (LU), combining our expertise in
Human Factors research and driving simulation. The project used a simulator-based
approach to closely replicate real-life driving conditions. This collaboration, together with DfT
and CCAV aimed to provide evidence-based insights that will inform future policy
development and contribute to safer implementation of automated driving technologies.
Method
The research was conducted using high-fidelity driving simulators at two locations:
University College London (UCL) and Loughborough University (LU), with 97 participants
representing the general UK driving population. The study used a within-participants design
with eight trials per participant, involving two motorway scenarios—roadworks and
congestion—designed to simulate conditions that exceed the operational limits of the ALKS
and trigger planned takeover requests due to speed changes:
1. Roadworks Scenario: The ego vehicle (which the participant controls in the driving
simulator) drove at 68 mph in light traffic. After 2 to 4 minutes, a roadworks sign
appeared, prompting a takeover request. Participants were expected to decelerate
upon taking manual control in response to speed limit signs.
2. Congestion Scenario: The ego vehicle drove at 37 mph due to traffic congestion.
After 2 to 4 minutes, the congestion cleared triggering a takeover request. The
participants were expected to accelerate to match the surrounding traffic speed.
Participants were briefed on the study's aims and procedures and completed a pre-clinic
questionnaire assessing their familiarity with technology and attitudes toward automated
driving. After fitting the participants with eye-tracking glasses (and EEG caps at UCL), they
engaged in a practice drive to familiarise themselves with the simulator controls and were
assessed for simulator sickness.
Each trial involved an automated driving phase, during which participants were engaged in
one of the following NDRAs or "No NDRA" conditions:
Mobile Phone Activities:
• Watching a Film: Participants selected a 5-minute YouTube video from a range of
options (e.g., a TED talk, a nature documentary) to watch on a Google Pixel 6a
smartphone, which was cradled on the dashboard.
• Playing Tetris: Participants played Tetris on a handheld Google Pixel 6a smartphone.
The phone was placed on the passenger seat, and participants were instructed to
pick it up and start playing once the trial began.

Regaining Situational Awareness as a User in Charge: Responding to transition demands in
automated vehicles
5
Non-Technological Activities:
• Reading a Magazine: Participants chose from a selection of magazines (e.g., BBC
Top Gear, National Geographic) and were instructed to pick up the magazine from
the passenger seat and start reading once the trial began.
• Completing a Wordsearch or Sudoku: Participants were given the choice between a
word search puzzle book or a Sudoku book, along with a pen. The materials were
placed on the passenger seat, and participants were instructed to start solving the
puzzle once the trial began.
Motoric Activities:
• Drinking Water: Participants drank water from a disposable coffee cup with a lid,
placed in the cup holder beside the driver. They were instructed to take frequent small
sips throughout the drive.
• Simulated Eating of “Popcorn”: Participants mimicked the action of eating by
transferring cotton balls from a packet into a cup holder attached to their chest. This
simulated the action of eating popcorn, avoiding the mess and potential distractions
of real food.
In each trial, participants received instructions on how to carry out the NDRA (or were
informed there would be no NDRA). The system operated in self-driving mode, and
participants were told to take manual control after a takeover request, "as soon as you feel
ready and safe to do so."
Automated drives lasted 2-4 minutes, ending with a takeover request. Takeover requests
were signalled by an auditory beep and a visual alert on the Human Machine Interface (HMI).
Participants had 30 seconds to take control. At UCL, manual control was regained when the
participant used the steering wheel or pedals, while at LU, participants were required to say,
“Ready to drive” and a researcher gave manual control. Participants then drove manually
for 30 seconds before completing a questionnaire on workload and self-perceived SA.
Data Collection and Analysis
A comprehensive set of measures were employed to assess SA following a takeover request
during automated driving. The analysis was designed to investigate whether participants
fully disengaged from NDRAs, their visual attention patterns post-takeover, particularly
focusing on whether they looked at mirrors or other critical areas of the driving environment,
and whether participants took appropriate behavioural actions, such as adjusting speed
based on the scenario (which would indicate their comprehension of the reasons behind the
takeover request). Additionally, the quality of the takeover was assessed by observing any
signs of erratic steering, such as swerving or crashing, which could suggest either poor
control or insufficient SA.
GoPro cameras captured in-cab behaviours, allowing for detailed analysis of how
participants managed the transition from NDRA to driving. Participants’ interactions with the
NDRA, their disengagement process, and their subsequent driving performance were all
recorded. This comprehensive data collection approach enabled the analysis of how
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