Correction: A framework for testing independence between lane change and cooperative intelligent transportation system.

[This corrects the article DOI: 10.1371/journal.pone.0229289.].

Exploring drivers' mental workload and visual demand while using an in-vehicle HMI for eco-safe driving.

Eco-safe driving is a promising approach to improve road safety while reducing transport emissions. The application of an eco-safe driving system is feasible with the support of vehicle-to-vehicle/infrastructure technologies. To guarantee system usability and safety appropriateness, a key precondition is to ensure that driver mental workload and visual demands required for using the system are reasonable. This study explored how drivers' mental workload and visual demands were affected when driving with an eco-safe driving HMI (human-machine-interface). Four in-vehicle eco-safe HMI information conditions were evaluated, including baseline, advice only, feedback only, and advice & feedback. Two traffic scenarios (stop-sign intersection with traffic vs. stop-sign intersection without traffic) were simulated using an advanced driving simulator. Behavioural variables (e.g. brake force, acceleration), visual variables (e.g. blink metrics, pupil size) and subjective workload scores were collected from 36 licensed Australian drivers. The experiment results showed that the HMI prompted drivers to apply a smooth and stable brake force when they approached the intersection and a smooth acceleration when they left the intersection. Drivers' mental workload indicated by visual measurements were consistent with their subjective reported workload levels. Drivers had a higher mental workload when they received and processed additional eco-safe information in the advice & feedback condition. An increase in mental workload induced by the in-vehicle cognitive task initiated more blink activities while the increase in visual demand caused by a complex road situation led to blink inhibition. The study shows the HMI could significantly promote eco-safe driving behaivours without causing excessive mental and visual workload of drivers.

A framework for testing independence between lane change and cooperative intelligent transportation system.

Cooperative Intelligent Transportation Systems (C-ITS) are being deployed in several cities around the world. We are preparing for the largest Field Operational Test (FOT) in Australia to evaluate C-ITS safety benefits. Two of the safety benefit hypotheses we formulated assume a dependency between lane changes and C-ITS warnings displayed on the Human Machine Interface (HMI) during safety events. Lane change detection is done by processing many predictors from several sensors at the time of the safety event. However, in our planned FOT, the participating vehicles are only equipped with the vehicle C-ITS and the IMU. Therefore, in this paper, we propose a framework to test lane change and C-ITS dependency. In this framework, we train a random forest classifier using data collected from the IMU to detect lane changes. Consequently, the random forest output probabilities of the testing data in case of C-ITS and control are used to construct a 2x2 contingency table. Then we develop a permutation test to calculate the null hypothesis needed to test the independence of the lane change during safety events and the C-ITS.

Evaluation of in-vehicle technologies to prevent unlicensed driving in Queensland and Victoria.

Unlicensed driving is a serious problem in many Australian states, with unlicensed driving-related crashes (UDC) costing up to $304 million per year in Queensland, and $176 million in Victoria. In this paper, we present a Benefit-Cost Ratio (BCR) analysis of a set of Intelligent Transportation Systems technologies aimed at preventing unlicensed driving by verifying the driver's identity through biometric technology, as well as the validity of their licence. Utilised together, the technology would essentially take the form of a licence interlock. The goal of this program of research (from which this paper stems) was to provide preliminary recommendations as to which technology is the most beneficial and should be implemented as part of a government-led program increasing the functionalities of electronic driving licences (EDL). The corresponding BCR analysis revealed that fingerprints and finger vascular patterns recognition technologies were found to systematically have the best BCRs. In regard to the most effective manner to implement the technology, a corresponding investigation with five scenarios revealed that the greatest benefits would be achieved with: (a) a mandatory system for all banned drivers (e.g., suspensions & disqualifications), and (b) a mandatory system for banned drivers under the age of 21 only. Scenario (b) performs extremely well, with returns of up to 16 times the investment with a simple fingerprint-based interlock. Although often more modest, all systems were found to have BCRs above 1 in all of the implementation scenarios except one. This paper further outlines the findings in regard to addressing the significant problem of unlicensed driving via emerging technologies.

Effects of an in-vehicle eco-safe driving system on drivers' glance behaviour.

We have designed a new in-vehicle eco-safe driving system and shown its effectiveness in prompting drivers to execute a fuel-saving and safe driving style (Vaezipour et al., 2018, submitted for publication). However, the system could also bring potential negative outcomes, i.e. driver distraction. This simulator study investigated drivers' glance behaviours as indicators of driver distraction when using our Eco-Safe Human-Machine-Interface (HMI). Four types of eco-safe information display conditions (baseline, advice only, feedback only, both advice and feedback) were tested on different traffic situations with varied road traffic complexity. Results showed that the eco-safe HMI system did not cause visual distraction. In contrast, the advice only or feedback only information improved forward gazing on the roadway. In addition, drivers tended to adapt their visual scanning strategies according to the traffic situations. In the car-following situation they paid longer glances to the forward roadway, while in the intersections they spent more time to look at the HMI system. The findings indicated that our eco-safe driving system improved drivers' eco-safe behaviours and meanwhile enhanced their visual attention on road while no evidence showed that drivers were distracted by it.

Driving behaviour while self-regulating mobile phone interactions: A human-machine system approach.

Mobile phone distracted driving is a recurrent issue in road safety worldwide. Recent research on driving behaviour of distracted drivers suggests that in certain circumstances drivers seem to assume safer behaviours while using a mobile phone. Despite a high volume of research on this topic, self-regulation by mobile phone distracted drivers is not well understood as many driving simulator experiments are designed to impose an equal level of distraction to participants being tested for their driving performance. The aim of this research was to investigate the relationship between self-regulatory secondary task performance and driving. By a driving simulator experiment in which participants were allowed to perform their secondary tasks whenever they feel appropriate, the driving performance of 35 drivers aged 18-29 years was observed under three phone conditions including non-distraction (no phone use), hands-free interactions and visual-manual interactions in the CARRS-Q advanced driving simulator. Drivers' longitudinal and lateral vehicle control observed across various road traffic conditions were then modelled by Generalized Estimation Equations (GEE) with exchangeable correlation structure accounting for heterogeneity resulting from multiple observations from the same driver. Results show that the extent of engagement in the secondary task influence both longitudinal and lateral control of vehicles. Drivers who engaged in a large number of hands-free interactions are found to select lower driving speed. In contrast, longer visual-manual interactions are found to result in higher driving speed among drivers self-regulating their secondary task. Among the road traffic conditions, drivers distracted by their self-regulated secondary tasks are found to select lower speeds along the s-curve compared to straight and motorway segments. In summary, the applied human-machine system approach suggests that road traffic demands play a vital role in both secondary task management and driving performance.