Marieke Hendrikje Martens

CityMobil: Human Factor Issues Regarding Highly Automated Vehicles on eLane

There are several human factor concerns with highly autonomous or semiautonomous driving, such as transition of control, loss of skill, and dealing with automated system errors. Four CityMobil experiments studied the eLane concept for dual-mode cars, and the results of one are described. The open eLane concept brings together road infrastructure and technical developments in vehicle automation to allow automated driving. The goal for the driving simulator experiment was to design and test the difference between a vocal and an acoustic user interface for a dual-mode vehicle driven both manually and automatically. In the experiment (n = 24), driver behavior was observed with a focus on the transition of control and the occurrence of system errors. Performance of transition of control was adequate for both interfaces at the beginning and end of an eLane. In the case of system failure, 15% of drivers failed to take control of the car in time for both interfaces. However, of those who did regain control, drivers with the vocal interface were faster. Moreover, a subjective questionnaire showed that the vocal interface was perceived as more positive than the acoustic interface. The study suggests that the vocal interface was preferred by participants and can be recommended for the human-machine interface of dual-mode vehicles, especially for providing warnings about system malfunctioning.There are several human factor concerns with highly autonomous or semiautonomous driving, such as transition of control, loss of skill, and dealing with automated system errors. Four CityMobil experiments studied the eLane concept for dual-mode cars, and the results of one are described. The open eLane concept brings together road infrastructure and technical developments in vehicle automation to allow automated driving. The goal for the driving simulator experiment was to design and test the difference between a vocal and an acoustic user interface for a dual-mode vehicle driven both manually and automatically. In the experiment (n = 24), driver behavior was observed with a focus on the transition of control and the occurrence of system errors. Performance of transition of control was adequate for both interfaces at the beginning and end of an eLane. In the case of system failure, 15% of drivers failed to take control of the car in time for both interfaces. However, of those who did regain control, drivers with the vocal interface were faster. Moreover, a subjective questionnaire showed that the vocal interface was perceived as more positive than the acoustic interface. The study suggests that the vocal interface was preferred by participants and can be recommended for the human–machine interface of dual-mode vehicles, especially for providing warnings about system malfunctioning.

The road to automated driving: Dual mode and human factors considerations

Recent technological developments have shown a transition from informative driving support systems to more automated vehicles. Although automated vehicles are designed to overcome limitations in human perception, decision making and response, there may be a downside to introducing these technologies. The downside is based on the new cooperation between the driver and the vehicle, leaving room for misinterpretation, overreliance on system performance and loss of situation awareness in case of requested transfer of control from the automated vehicle back to the driver. This article raises several human factors issues that are of importance when designing (semi-)automated vehicles, such as: the driver as a system monitor, situation awareness and system limitations. Various implications for the design of automated systems are discussed.

A human factors perspective on automated driving

ABSTRACT Automated driving can fundamentally change road transportation and improve quality of life. However, at present, the role of humans in automated vehicles (AVs) is not clearly established. Interviews were conducted in April and May 2015 with 12 expert researchers in the field of human factors (HFs) of automated driving to identify commonalities and distinctive perspectives regarding HF challenges in the development of AVs. The experts indicated that an AV up to SAE Level 4 should inform its driver about the AVs capabilities and operational status, and ensure safety while changing between automated and manual modes. HF research should particularly address interactions between AVs, human drivers and vulnerable road users. Additionally, driver-training programmes may have to be modified to ensure that humans are capable of using AVs. Finally, a reflection on the interviews is provided, showing discordance between the interviewees’ statements – which appear to be in line with a long history of HFs research – and the rapid development of automation technology. We expect our perspective to be instrumental for stakeholders involved in AV development and instructive to other parties.

Effects of stimulant drugs on actual and simulated driving: perspectives from four experimental studies conducted as part of the DRUID research consortium

The Integrated Project DRUID (Driving under the Influence of Drugs, Alcohol and Medicines) involved researchers from more than 20 European countries. It aimed to gain new insights to the degree of impairment caused by psychoactive drugs and their actual impact on road safety. Part of this large research program that was conducted between 2006 and 2011 has been devoted to the assessment of stimulant drug effects on driving performance in experimental, placebo-controlled studies. These studies are presented in the current issue of psychopharmacology and focus on single-dose effects of MDMA (Bosker et al. 2012) and dexamphetamine (Hjalmdahl et al. 2012) on driving performance before and after a night of sleep deprivation and on the effects of MDMA (Veldstra et al. 2012) and dexamphetamine (Simons et al. 2012) with and without alcohol. The major objective of these studies was to provide scientific basis for harmonized pan-European regulations of driving under the influence (DUI) of stimulants.

Behavioural adaptation and acceptance

One purpose of Intelligent Vehicles is to improve road safety, throughput, and emissions. However, the predicted effects are not always as large as aimed for. Part of this is due to indirect behavioral changes of drivers, also called behavioral adaptation. Behavioral adaptation (BA) refers to unintended behavior that arises following a change to the road traffic system. Qualitative models of behavioral adaptation (formerly known as risk compensation) describe BA by the change in the subjectively perceived enhancement of the safety margins. If a driver thinks that the system is able to enhance safety and also perceives the change in behavior as advantageous, adaptation occurs. The amount of adaptation is (indirectly) influenced by the driver personality and trust in the system. This also means that the amount of adaptation differs between user groups and even within one driver or changes over time. Examples of behavioral change are the generation of extra mobility (e.g., taking the car instead of the train), road use by “less qualified” drivers (e.g., novice drivers), driving under more difficult conditions (e.g., driving on slippery roads), or a change in distance to the vehicle ahead (e.g., driving closer to a lead vehicle with ABS). In effect predictions, behavioral adaptation should be taken into account. Even though it may reduce beneficial effects, BA (normally) does not eliminate the positive effects. How much the effects are reduced depends on the type of ADAS, the design of the ADAS, the driver, the current state of the driver, and the local traffic and weather conditions

Drowsy drivers' under-performance in lateral control: How much is too much? Using an integrated measure of lateral control to quantify safe lateral driving.

Internationally, drowsy driving is associated with around 20% of all crashes. Despite the development of different detection methods, driver drowsiness remains a disconcerting public health issue. Detection methods can estimate drowsiness by directly measuring the physiology of the driver, or they can measure the effect that drowsiness has on the state of the vehicle due to the behavioural changes that drowsiness elicits in the driver. The latter has the benefit that it could measure the net effect that drowsiness has on driving performance which links to the actual safety risk. Fusing multiple sources of driving performance indicators like lane position and steering wheel metrics in order to detect drowsiness has recently gained increased attention. However, not much research has been conducted with regard to using integrated measures to detect increased drowsiness within an individual driver. Different levels of drowsiness are also rarely classified in terms of safe or unsafe. In the present study, we attempt to slowly induce drowsiness using a monotonous driving task in a simulator, and fuse lane position and steering wheel angle data into a single measure for lateral control performance. We argue that this measure is applicable in real-time detection systems, and quantitatively link it to different levels of drowsiness by validating it to two established drowsiness metrics (KSS and PERCLOS). Using level of drowsiness as a surrogate for safety we are then able to set simple criteria for safe and unsafe lateral control performance, based on individual driving behaviour.

Assessing drivers ability to carry out headway advice in motorway car driving

In order to improve throughput on motorways, a headway advisory system is being developed. This system could enable drivers to choose their headway differently by providing in-vehicle advice. How well are drivers able to follow a time or a distance headway advice and what effect has vehicle speed and advised headway on their ability? To test this, a group of 20 participants completed nine headway adjustment tasks in a driving simulator experiment. Half of the participants received a time headway advice and half received a distance headway advice. The absolute difference between the advised and the chosen headway acted as a performance measure. In the experiment no performance difference could be found between the driver’s carrying out target headway and those carrying out distance headway advice. Furthermore increasing vehicle speed significantly reduced the driver’s ability to carry out headway advice. This effect was not found for target headway. High inter-driver difference in headway estimation led to high standard deviations in estimation error. Apart from reducing the average estimation error, the support system under development should also focus on reducing the variance in chosen headways.

How can humans understand their automated cars? HMI principles, problems and solutions

As long as vehicles do not provide full automation, the design and function of the Human Machine Interface (HMI) is crucial for ensuring that the human “driver” and the vehicle-based automated systems collaborate in a safe manner. When the driver is decoupled from active control, the design of the HMI becomes even more critical. Without mutual understanding, the two agents (human and vehicle) will fail to accurately comprehend each other’s intentions and actions. This paper proposes a set of design principles for in-vehicle HMI and reviews some current HMI designs in the light of those principles. We argue that in many respects, the current designs fall short of best practice and have the potential to confuse the driver. This can lead to a mismatch between the operation of the automation in the light of the current external situation and the driver’s awareness of how well the automation is currently handling that situation. A model to illustrate how the various principles are interrelated is proposed. Finally, recommendations are made on how, building on each principle, HMI design solutions can be adopted to address these challenges.

Interface Concepts for Intent Communication from Autonomous Vehicles to Vulnerable Road Users

This paper presents six interface concepts for Autonomous Vehicles to communicate their intention to Vulnerable Road Users. The concepts were designed to be scalable and versatile, and attempt to address some of the limitations of existing concepts towards an unambiguous communication. The interfaces exist currently as initial concepts generated from brainstorming sessions and are in the process of being validated through prototype development and controlled studies.

Changes in Trust after Driving Level 2 Automated Cars

Overtrust and undertrust are major issues with partially automated vehicles. Ideally, trust should be calibrated ensuring that drivers’ subjective feelings of safety match the objective reliability of the vehicle. In the present study, we examined if drivers’ trust toward Level 2 cars changed after on-road experience. Drivers’ self-reported trust was assessed three times: before having experience with these vehicles, immediately after driving two types of vehicles, and two weeks after the driving experience. Analysis of the results showed major changes in trust scores after the on-road driving experience. Before experiencing the vehicles, participants tended to overestimate the vehicle capabilities. Afterwards they had a better understanding of vehicles’ limitations, resulting in better calibrated trust.