Does haptic steering guidance instigate speeding? A driving simulator study into causes and remedies

Shared control versus traded control in driving: a debate around automation pitfalls

A major question in human-automation interaction is whether tasks should be traded or shared between human and automation. This work presents reflections-which have evolved through classroom debates between the authors over the past 10 years-on these two forms of human-automation interaction, with a focus on the automated driving domain. As in the lectures, we start with a historically informed survey of six pitfalls of automation: (1) Loss of situation and mode awareness, (2) Deskilling, (3) Unbalanced mental workload, (4) Behavioural adaptation, (5) Misuse, and (6) Disuse. Next, one of the authors explains why he believes that haptic shared control may remedy the pitfalls. Next, another author rebuts these arguments, arguing that traded control is the most promising way to improve road safety. This article ends with a common ground, explaining that shared and traded control outperform each other at medium and low environmental complexity, respectively. Practitioner summary: Designers of automation systems will have to consider whether humans and automation should perform tasks alternately or simultaneously. The present article provides an in-depth reflection on this dilemma, which may prove insightful and help guide design. Abbreviations: ACC: Adaptive Cruise Control: A system that can automatically maintain a safe distance from the vehicle in front; AEB: Advanced Emergency Braking (also known as Autonomous Emergency Braking): A system that automatically brakes to a full stop in an emergency situation; AES: Automated Evasive Steering: A system that automatically steers the car back into safety in an emergency situation; ISA: Intelligent Speed Adaptation: A system that can limit engine power automatically so that the driving speed does not exceed a safe or allowed speed.

Method-oriented systematic review on the simple scale for acceptance measurement in advanced transport telematics

Acceptance intuitively is a precondition for the adaptation and use of technology. In this systematic review, we examine academic literature on the “simple scale for acceptance measurement” provided by Van der Laan, Heino, and de Waard (1997). This measure is increasingly applied in research on mobility systems without having been thoroughly analysed. This article aims to provide such a critical analysis. We identified 437 unique references in three aggregated databases and included 128 articles (N = 6,058 participants) that empirically applied the scale in this review. The typical study focused on a mobility system using a within-subjects design in a driving simulator in Europe. Based on quality indicators of transparent study aim, group allocation procedure, variable definitions, sample characteristics, (statistical) control of confounders, reproducibility, and reporting of incomplete data and test performance, many of the 128 articles exhibited room for improvements (44% below.50; range 0 to 1). Twenty-eight studies (22%) reported reliability coefficients providing evidence that the scale and its sub-scales produce reliable results (median Cronbach’s α >.83). Missing data from the majority of studies limits this conclusion. Only 2 out of 10 factor analyses replicated the proposed two-dimensional structure questioning the use of these sub-scales. Correlation results provide evidence for convergent validity of acceptance, usefulness, and satisfying with limited confidence, since only 14 studies with a median sample size of N = 40 reported correlation coefficients. With these results, the scale might be a valuable addition for technology attitude research. Firstly, we recommend thorough testing for a better understanding of acceptance, usefulness, and satisfying. Secondly, we suggest to report scale results more transparently and rigorously to enable meta-analyses in the future. The study protocol is available at the Open Science Framework (https://osf.io/j782c/).

Guidance-oriented advanced curve speed warning system in a connected vehicle environment

Connected Vehicles (CV) technology has been used to address safety issues on highway horizontal curves. Existing curve warning systems are either using curve warning signs or providing drivers with an in-vehicle curve warning message in advance, allowing drivers to adjust their speed prior to the vehicle entering the curve. In practice, drivers might be compliant before entering the curve but may pick up the speed in the curve. Therefore, it remains a problem that existing curve warning systems are not able to guide drivers by providing necessary speed warnings through the entire course of approaching, entering, navigating, and leaving horizontal curves. Therefore, the objective of this study is to improve curve speed compliance by proposing a guidance-oriented Advanced Curve Speed Warning system (Advanced-CSW) with a focus on providing guided curve speed messages throughout the horizontal curves. The Advanced-CSW system is based on Dedicated Short-Range Communication (DSRC) enabling vehicle-infrastructure (V2I) communication. Anytime the vehicle is speeding, the guided message will be displayed until the vehicle's speed is within compliant range. Drivers who use the Advanced-CSW can receive multiple guided messages from the in-vehicle heads-up display through the entire course of navigating through horizontal curves. Thirty participants are recruited to perform the driving experiment on the simulator of driving through a series of horizontal curves under various geometric, roadway and traffic conditions. These conditions include different curve severity, illumination, and pavement wetness levels. The Advanced-CSW system's performance was evaluated in terms of the speed difference, which measures the gap between the in-curve mean speed and curve advisory speed. The results were compared with the performance of speed difference by driving with CSW or CSO through the entire curve. The experiment data was modeled using the mixed linear model with random effects, which includes the individual's driving behavior. In summary, when male drivers navigate through the horizontal curves under different curve speed warning systems, their speed compliance is significantly increased with continuous and guided messages provided in comparison with the speed compliance under the one-time curve speed warning message and the curve sign only. Female drivers improve their speed compliance in the curve by using curve signs only comparing to using one-time curve speed warning message or continuous guided curve speed warning messages. Also, male drivers' speed differences by using the guided system are significantly reduced by 6.53∼7.68 mi/h compared to driving with curve signs only or one-time curve speed warning message. In addition, there is also a speed reduction of 1.81 mi/h if male drivers receiving continuous guided messages in the curve during the daytime than during the nighttime. The proposed adaptive system based on that is adaptive to the vehicle's real-time speed and location by providing a new direction in designing effective curve warning systems. The speed-guided messages through the entire course of approaching, entering, navigating, and leaving horizontal curves can solve the current issue of speed incompliance by using the existing curve warning systems.

Human-like driving behaviour emerges from a risk-based driver model

Current driving behaviour models are designed for specific scenarios, such as curve driving, obstacle avoidance, car-following, or overtaking. However, humans can drive in diverse scenarios. Can we find an underlying principle from which driving behaviour in different scenarios emerges? We propose the Driver’s Risk Field (DRF), a two-dimensional field that represents the driver’s belief about the probability of an event occurring. The DRF, when multiplied with the consequence of the event, provides an estimate of the driver’s perceived risk. Through human-in-the-loop and computer simulations, we show that human-like driving behaviour emerges when the DRF is coupled to a controller that maintains the perceived risk below a threshold-level. The DRF model predictions concur with driving behaviour reported in literature for seven different scenarios (curve radii, lane widths, obstacle avoidance, roadside furniture, car-following, overtaking, oncoming traffic). We conclude that our generalizable DRF model is scientifically satisfying and has applications in automated vehicles.

Most driver models were designed for specific scenario. Here, the authors developed a driver behaviour model that can be applied to multiple scenarios and show that human-like driving behaviour emerges when the Driver’s Risk Field is coupled to a controller that maintains the perceived risk below a threshold level.

Know-how or know-why? The role of hybrid electric vehicle drivers' acquisition of eco-driving knowledge for eco-driving success

Hybrid electric vehicles (HEVs) can contribute to sustainable transport. Yet, their real-world energy efficiency depends on HEV drivers' eco-driving behaviour. Eco-driving knowledge is key for successful eco-driving. The present research focused on the role of perceived strategy knowledge (know-how) versus technical system knowledge (know-why) in a study with 121 HEV drivers. The relationship between knowledge components and knowledge acquisition processes, as well as fuel efficiency, were examined. Structural equation modelling results indicated that perceived strategy knowledge was related to acquisition by testing (i.e., interacting with the vehicle and its interfaces) and reading (i.e., manuals, books and websites) while technical system knowledge was only related to acquisition by reading. In contrast to technical system knowledge, perceived strategy knowledge was no significant predictor of fuel efficiency. The results indicated that emphasis should be put into promoting technical system knowledge (e.g., by tutoring systems) to support motivated drivers' in achieving higher fuel efficiency.

What determines drivers' speed? A replication of three behavioural adaptation experiments in a single driving simulator study

We conceptually replicated three highly cited experiments on speed adaptation, by measuring drivers' experienced risk (galvanic skin response; GSR), experienced task difficulty (self-reported task effort; SRTE) and safety margins (time-to-line-crossing; TLC) in a single experiment. The three measures were compared using a nonparametric index that captures the criteria of constancy during self-paced driving and sensitivity during forced-paced driving. In a driving simulator, 24 participants completed two forced-paced and one self-paced run. Each run held four different lane width conditions. Results showed that participants drove faster on wider lanes, thus confirming the expected speed adaptation. None of the three measures offered persuasive evidence for speed adaptation because they failed either the sensitivity criterion (GSR) or the constancy criterion (TLC, SRTE). An additional measure, steering reversal rate, outperformed the other three measures regarding sensitivity and constancy, prompting a further evaluation of the role of control activity in speed adaptation. Practitioner Summary: Results from a driving simulator experiment suggest that it is not experienced risk, experienced effort or safety margins that govern drivers' choice of speed. Rather, our findings suggest that steering reversal rate has an explanatory role in speed adaptation.