Drivers’ Evaluation of Different Automated Driving Styles: Is It both Comfortable and Natural?

Objective: This study investigated users’ subjective evaluation of three highly automated driving styles, in terms of comfort and naturalness, when negotiating a UK road in a high-fidelity, motion-based, driving simulator. Background: Comfort and naturalness are thought to play an important role in contributing to users’ acceptance and trust of automated vehicles (AVs), although not much is understood about the types of driving style which are considered comfortable or natural. Method: A driving simulator study, simulating roads with different road geometries and speed limits, was conducted. Twenty-four participants experienced three highly automated driving styles, two of which were recordings from human drivers, and the other was based on a machine learning (ML) algorithm, termed Defensive, Aggressive, and Turner respectively. Participants evaluated comfort or naturalness of each driving style, for each road segment, and completed a Sensation Seeking (SS) questionnaire, which assessed their risk-taking propensity. Results: Participants regarded human-like driving styles as more comfortable and natural, compared with the less human-like, ML-based, driving controller. However, between the two human-like controllers, only the Defensive style was considered comfortable, especially for the more challenging road environments. Differences in preference for controller by driver trait were also observed, with the Aggressive driving style evaluated as more natural by the high sensation seekers. Conclusion: Participants were able to distinguish between human- and machine-like AV controllers. A range of psychological concepts must be considered for the subjective evaluation of controllers. Application: Knowing how different driver groups evaluate automated vehicle controllers is important to design more acceptable systems in the future.

Measuring Drivers’ Physiological Response to Different Vehicle Controllers in Highly Automated Driving (HAD): Opportunities for Establishing Real-Time Values of Driver Discomfort

This study investigated how driver discomfort was influenced by different types of automated vehicle (AV) controllers, compared to manual driving, and whether this response changed in different road environments, using heart-rate variability (HRV) and electrodermal activity (EDA). A total of 24 drivers were subjected to manual driving and four AV controllers: two modelled to depict “human-like” driving behaviour, one conventional lane-keeping assist controller, and a replay of their own manual drive. Each drive lasted for ~15 min and consisted of rural and urban environments, which differed in terms of average speed, road geometry and road-based furniture. Drivers showed higher skin conductance response (SCR) and lower HRV during manual driving, compared to the automated drives. There were no significant differences in discomfort between the AV controllers. SCRs and subjective discomfort ratings showed significantly higher discomfort in the faster rural environments, when compared to the urban environments. Our results suggest that SCR values are more sensitive than HRV-based measures to continuously evolving situations that induce discomfort. Further research may be warranted in investigating the value of this metric in assessing real-time driver discomfort levels, which may help improve acceptance of AV controllers.

Vehicle-in-the-Loop in Global Coordinates for Advanced Driver Assistance System

Most vehicle controllers are developed and verified with V-model. There are several traditional methods in the automotive industry called “X-in-the-Loop (XIL)”. However, the validation of advanced driver assistance system (ADAS) controllers is more complicated and needs more environmental resources because the controller interacts with the external environment of the vehicle. Vehicle-in-the-Loop (VIL) is a recently being developed approach for simulating ADAS vehicles that ensures the safety of critical test scenarios in real-world testing using virtual environments. This new test method needs both properties of traditional computer simulations and real-world vehicle tests. This paper presents a Vehicle-in-the-Loop topology for execution in global Coordinates system. Also, it has a modular structure with four parts: synchronization module, virtual environment, sensor emulator and visualizer, so each part can be developed and modified separately in combination with other parts. This structure of VIL is expected to save maintenance time and cost. This paper shows its acceptability by testing ADAS on both a real and the VIL system.

Understanding the Roles of Remoteness and Indigenous Status in Rural and Remote Road Trauma in North Queensland: Using a Mixed-Methods Approach

Road trauma is a significant health problem in rural and remote regions of Australia, particularly for Indigenous communities. This study aims to identify and compare the circumstances leading to (proximal causation) and social determinants of (distal causation) crashes of Indigenous and non-Indigenous people in these regions and their relation to remoteness. This is a topic seriously under-researched in Australia. Modelled on an earlier study, 229 persons injured in crashes were recruited from local health facilities in rural and remote North Queensland and interviewed, mainly by telephone, according to a fixed protocol which included a detailed narrative of the circumstances of the crash. A qualitative analysis of these narratives identified several core themes, further explored statistically in this sample, supplemented by participants in the earlier study with compatible questionnaire data, designed to determine which factors were more closely associated with Indigenous status and which with remoteness. Indigenous participants were less often vehicle controllers, more likely to have recently been a drink driver or passenger thereof; to be unemployed, unlicensed, distracted or fatigued before the crash, alcohol dependent and have lower perceived social, but not personal, locus of control in a traffic crash than non-Indigenous persons. Differences between Indigenous and non-Indigenous participants are largely ascribable to hardship and transport disadvantage due to lack of access to licensing and associated limitations on employment opportunities. Based on these findings, a number of policy recommendations relating to educational, enforcement and engineering issues have been made.

A Comparative Study of Different Objectives Functions for the Minimal Fuel Drive Cycle Optimization in Autonomous Vehicles

With the advent of self-driving autonomous vehicles, vehicle controllers are free to drive their own velocities. This feature can be exploited to drive an optimal velocity trajectory that minimizes fuel consumption. Two typical approaches to drive cycle optimization are velocity smoothing and tractive energy minimization. The former reduces accelerations and decelerations, and hence, it does not require information of vehicle parameters and resistance forces. On the other hand, the latter reduces tractive energy demand at the wheels of a vehicle. In this work, utilizing an experimentally validated full vehicle simulation software, we show that for conventional gasoline vehicles the lower energy velocity trajectory can consume as much fuel as the velocity smoothing case. This implies that the easily implementable, vehicle agnostic velocity smoothing optimization can be used for velocity optimization rather than the nonlinear tractive energy minimization, which results in a pulse-and-glide trajectory.