Monetising human impacts. CLeMM: Customer Led Monetising Method

Executive Summary: This study has developed a Customer Led Monetising Method (CLeMM) to assess the monetary value of the human impacts of Highways England's operational services. CLeMM is simple in concept, easy to use and adaptable to a wide variety of situations enabling Highways England to use it in surveys, focus groups and other customer consultations for the widest range of stakeholders. The method for using CLeMM is described in the CLeMM Guide

State of the art on measuring driver state and technology-based risk prevention and mitigation Findings from the i-DREAMS project

Advanced vehicle automation and the incorporation of more digital technologies in the task of driving, bring about new challenges in terms of the operator/vehicle/environment framework, where human factors play a crucial role. This paper attempts to consolidate the state-of-the-art in driver state measuring, as well as the corresponding technologies for risk assessment and mitigation, as part of the i-DREAMS project. Initially, the critical indicators for driver profiling with regards to safety risk are identified and the most prominent task complexity indicators are established. This is followed by linking the aforementioned indicators with efficient technologies for real-time measuring and risk assessment and finally a brief overview of interventions modules is outlined in order to prevent and mitigate collision risk. The results of this review will provide an overall multimodal set of factors and technologies for driver monitoring and risk mitigation, essential for road safety researchers and practitioners worldwide<br

Child pedestrian safety en route to and from rural schools: A case study

This research examines the safety hazards faced by child pedestrians at rural schools within the Waipa District. The main objectives of this research were to identify hazards child pedestrians face, to identify current counter-measures to these hazards, and to evaluate the regulations and policies pertaining to these counter-measures and child pedestrian safety. Meeting these objectives then allowed the design of possible counter-measures to the hazards faced by rural child pedestrians. The ultimate goal of this research was to improve child pedestrian safety at rural schools

From Driver to Supervisor: Comparing Cognitive Load and EEG-based Attentional Resource Allocation across Automation Levels

With increasing automation, drivers' roles transition from active operators to passive system supervisors, affecting their behaviour and cognitive processes. This study addresses the attentional resource allocation and subjective cognitive load during manual, SAE Level 2, and SAE Level 3 driving in a realistic environment. An experiment was conducted on a test track with 30 participants using a prototype automated vehicle. While driving, participants were subjected to a passive auditory oddball task and their electroencephalogram was recorded. The study analysed the amplitude of the P3a event-related potential component elicited by novel environmental stimuli, an objective measure of attentional resource allocation. The subjective cognitive load was assessed using the NASA Task Load Index. Results showed no significant difference in subjective cognitive load between manual and Level 2 driving, but a decrease in subjective cognitive load in Level 3 driving. The P3a amplitude was highest during manual driving, indicating increased attentional resource allocation to environmental sounds compared to Level 2 and Level 3 driving. This may suggest that during automated driving, drivers allocate fewer attentional resources to processing environmental information. It remains unclear whether the decreased processing of environmental stimuli in automated driving is due to top-down attention control (leading to attention withdrawal) or bottom-up competition for resources induced by cognitive load. This study provides novel empirical evidence on resource allocation and subjective cognitive load in automated driving. The findings highlight the importance of managing drivers' attention and cognitive load with implications for enhancing automation safety and the design of user interfaces.Comment: 17 pages, 4 figure

Effects of the roadside visual environment on driver wellbeing and behaviour – a systematic review

The view that drivers have from the road can be enjoyable or disturbing, stressful or relaxing, distracting or fatiguing. Road planning guidelines balance aesthetical and safety considerations but are rarely grounded on empirical evidence. This paper reviews evidence on the effects of the roadside visual environment on the wellbeing and behaviour of drivers, focusing on natural and built elements external to the road, i.e. excluding road geometry, design, conditions, and users. Standardized information was extracted from 50 studies. These studies have used experiments involving participants watching videos or driving a simulator or instrumented vehicle, usually with unrepresentative samples (mostly males, young age groups, and students). Most evidence is related to the driving task (e.g. distraction, fatigue), not to wider aspects of driver wellbeing (e.g. stress recovery), and to safety issues, not aesthetical ones. There is increased evidence for monotonous views (linked to fatigue), roadside vegetation (linked mainly to a reduction of stress and risky driving behaviours, but depending on the characteristics of the vegetation) and advertisements (linked to distraction, but depending on advertisement type and other variables). A few studies have looked at other elements of the built environment (memorials, drones, utility poles, wind turbines), with mixed evidence on distraction and safety behaviour. The links between continued exposure to certain types of views and car commuter stress have not been studied. There is little evidence for developing countries or differences by gender, visual impairment, trip purpose, and type of vehicle

Driver\u27s Behavior and Workload Assessment for New In-Vehicle Technologies Design

Innovative technology can induce improvement in road safety, as long as its acceptability and its adequacy are checked, taking into account the diversified driver’s population needs and functional abilities through a Human Centred Design process. Relevant methodology has to be developed in this purpose. Evaluation of the driver’s mental workload is an important parameter, complementary to objective ones such as control of the vehicle and driver’s visual strategies. This paper describes experiments conducted in the framework of the European project AIDE aiming at validating the DALI (Driving Activity Load Index), a tool set up to allow evaluation of mental workload while using in-vehicle systems; the main results and conclusion from this approach are presented

Reduced Fuel Emissions through Connected Vehicles and Truck Platooning

Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication enable the sharing, in real time, of vehicular locations and speeds with other vehicles, traffic signals, and traffic control centers. This shared information can help traffic to better traverse intersections, road segments, and congested neighborhoods, thereby reducing travel times, increasing driver safety, generating data for traffic planning, and reducing vehicular pollution. This study, which focuses on vehicular pollution, used an analysis of data from NREL, BTS, and the EPA to determine that the widespread use of V2V-based truck platooning—the convoying of trucks in close proximity to one another so as to reduce air drag across the convoy—could eliminate 37.9 million metric tons of CO2 emissions between 2022 and 2026

Enhancing Acceptance and Trust in Automated Driving trough Virtual Experience on a Driving Simulator

As vehicle driving evolves from human-controlled to autonomous, human–machine interaction ensures intuitive usage as well as the feedback from vehicle occupants to the machine for optimising controls. The feedback also improves understanding of the user satisfaction with the system behaviour, which is crucial for determining user trust and, hence, the acceptance of the new functionalities that aim to improve mobility solutions and increase road safety. Trust and acceptance are potentially the crucial parameters for determining the success of autonomous driving deployment in wider society. Hence, there is a need to define appropriate and measurable parameters to be able to quantify trust and acceptance in a physically safe environment using dependable methods. This study seeks to support technical developments and data gathering with psychology to determine the degree to which humans trust automated driving functionalities. The primary aim is to define if the usage of an advanced driving simulator can improve consumer trust and acceptance of driving automation through tailor-made studies. We also seek to measure significant differences in responses from different demographic groups. The study employs tailor-made driving scenarios to gather feedback on trust, usability and user workload of 55 participants monitoring the vehicle behaviour and environment during the automated drive. Participants’ subjective ratings are gathered before and after the simulator session. Results show a significant increase in trust ensuing the exposure to the driving automation functionalities. We quantify this increase resulting from the usage of the driving simulator. Those less experienced with driving automation show a higher increase in trust and, therefore, profit more from the exercise. This appears to be linked to the demanded participant workload, as we establish a link between workload and trust. The findings provide a noteworthy contribution to quantifying the method of evaluating and ensuring user acceptance of driving automation. It is only through the increase of trust and consequent improvement of user acceptance that the introduction of the driving automation into wider society will be a guaranteed success

Investigating Attention Modeling Differences between Older and Younger Drivers

As in-vehicle technologies (IVTs) grow in both popularity and complexity, the question of whether these IVTs improve, or hinder, driver performance has gained more attention. The ability to predict when a driver will be looking at the road or a display on the car’s dashboard or center console is crucial to understanding the impact of the recent tech-heavy trend in car designs on safety and the extent to which IVTs compete with the primary driving task for visual resources. The SEEV model of visual attention has been shown to be able to predict the probability of attending an area if interest (AOI) while driving based on the salience (SEEV-S) of visual stimuli, the effort (SEEV-Ef) required to shift attention between locations, the expectancy (SEEV-Ex) that information will be found at a specific location within the visual field, and the value (SEEV-V) of the information found at that location relative to the task(s) being performed. This study compared older and younger adult SEEV models calculated using eye tracking during a series of simulated driving scenarios with differing levels of effort, expectancy, and value placed on the primary driving task and a secondary in-vehicle task (IVT) to be done on the center console while maintaining lane position and speed. No significant effect of the effort variable was found, likely due to the cues used in our experiment not requiring head or torso rotation to access. Good model fits for both older and younger adults were found, with younger adults having greater weight on the dashboard AOI than older adults when the driving task was prioritized