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Automated vehicles are an emerging technology that operate with differing levels of automatic control (SAE levels). The current study explored participants’ acceptance of a conditional (Level 3) automated vehicle (AV) before and after riding as a passenger for 10 min on open, public roads in uncontrolled traffic. Additionally, participants were asked to rate the riskiness (perceived risk) of a variety of vehicle maneuvers, such as turning, accelerating, and braking when approaching an intersection. We predicted that participants would report higher acceptance ratings and lower perceived risk ratings after experiencing the AV compared to pre-trip ratings. Further, we predicted that participants riding in the front-passenger seat would report higher ratings for risk compared to participants sitting in the rear-passenger or rear-driver seats. Sixty participants from South-East Queensland, Australia (aged 21–82 years; Mage = 45.78; 23 female) took part in the study. Compared to pre-trip responses, participants reported statistically higher ratings for acceptance, as well as statistically lower ratings for perceived risk with respect to specific vehicle maneuvers performed while in automated mode. Differences were detected between seats for perceived risk, but these results were less clear. Increases in acceptance and decreases in risk were also detected as the number of false hazards detected by the vehicle increased. Overall, these findings suggest that acceptance towards AVs may increase, and expectations of risk related to AV maneuvers may decrease, after participants have experienced the vehicles firsthand, on an open, public road in an uncontrolled traffic environment.

Highly automated driving (HAD) is a part of the future ways for the "intelligent" road mobility. In this framework, some studies have shown that drivers' situational awareness decreases when using HAD. In this HAD context, drivers can engage non driving tasks as reading or sleeping. These non-driving tasks lead to increased reaction time in case of hazardous situations or risky events (hardware, sensor, actuator failures, or front obstacle or crashes, or dense traffic congestion, or adverse conditions). In this paper, a global risk indicator is proposed using local information coming from surrounding vehicles or infrastructures (V2X communication). This paper shows firstly the gain of such a global risk indicator comparatively to the local one, and secondly its impact on the behaviour of both the autonomous car and the driver.

The ANDS study provided access to a large dataset. An interactive visualisation interface was developed to help researchers to investigate such a large dataset. The interface extends the one developed by SHRP2, and is aimed to provide researchers with a flexible tool allowing analyses outside predefined events of interest. This first version of the interface is presented in this abstract, and additional functions are suggested for future implementation.

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 mana

Unmanned Aerial Vehicles (UAVs) offer a considerable potential for road safety stakeholders; they have been already used for tasks such as assisting first responders. This paper reports on a small-scale prototyping effort to use UAVs to measure speeding and tailgating on major urban roads. One hour of UAV footage was acquired and a custom vehicle detection system was programmed. From this, we developed methods to extract vehicles’ speed and headways. Validation information was extracted from TMR induction loop present near the testing site.

The Australian Naturalistic Driving Study provides immense opportunities for further understanding driver behaviour. Data collection being now completed, and a large subset of the data becoming available to researchers, it becomes important to look at what is available in the data. This paper presents a first attempt to develop a scalable approach for analysing data, by looking at the particular case of vehicle speed and driving location. A preliminary spatial database was created using ~20,000 New South Wales trips, and GPS points were map matched to Australian roads. This approach provides opportunities for studying locations where NSW drivers over-speed.

Improving the safety at passive rail crossings is an ongoing issue worldwide. These crossings have no active warning systems to assist drivers’ decision-making and are completely reliant on the road user perceiving the approach of a train to decide whether to enter a crossing or not. This study aimed to better understand drivers’ judgements regarding approaching trains and their perceptions of safe crossing. Thirty-six participants completed a field-based protocol that involved detecting and judging the speeds of fast moving trains. They were asked to report when they first detected an approaching train, when they could first perceive it as moving, as well as providing speed estimates and a decision regarding when it would not be safe to cross. Participants detected the trains ∼2 km away and were able to perceive the trains as moving when they were 1.6 km away. Large differences were observed between participants but all could detect trains within the range of the longest sighting distances required at passive level crossings. Most participants greatly underestimated travelling speed by at least 30%, despite reporting high levels of confidence in their estimates. Further, most participants would have entered the crossing at a time when the lights would have been activated if the level crossing had been protected by flashing lights. These results suggest that the underestimation of high-speed trains could have significant safety implications for road users’ crossing behaviour, particularly as it reduces the amount of time and the safety margins that the driver has to cross the rail crossing.

This paper reports on a three year multidisciplinary research project for Motor Accident Insurance Commission investigating the relationship between rear-end crashes and unsafe car-following behaviours in Queensland. The methodology combined on-road data collection, crash data and self-report behavioural data. On-road measurements (3 million data-points) confirmed that tailgating is a major contributor to rear-end crashes. Results from the community survey (N=495) indicated that most drivers expressed the belief that they are keeping safe distances. However, objective measurements from the larger on-road analysis showed that this is generally not the case. Our findings and the policy implications will be discussed in detail.

Eco-driving is about energy efficient use of vehicles. There are many approaches to improve eco-driving. Eco-driving driving style is hard to learn even with the help of driving assistance systems. This paper presents an immersive driving simulation training tool to support eco-driving training. We address the following questions: - (1) How gamification concepts can improve eco-driving evaluation, training and adoption in simulated environments and - (2) How to setup such elements in a 3D immersive driving simulator. We present an implementation of gamification concepts in a driving simulator architecture built upon pro-SiVIC software and a 3D Helmet Mounted Display. The gamification functions is then used to motivate drivers to be eco-friendly. We conclude with future work and open issues.

Unlicensed driving is a serious problem in many Australian states, with unlicensed drivingrelated crashes (UDC) costing up to $300 million per year in Queensland, and $171 million in Victoria. In this paper, we present a Benefit-Cost Ratio (BCR) analysis of ITS technologies aimed at preventing unlicensed driving by verifying the driver’s identity through biometric technology, as well as the validity of their licence; essentially a form of licence interlock. The goal is 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 Queensland electronic driving licenses (EDL). Fingerprints and finger vascular patterns recognition technologies are found to systematically have the best BCRs. The most beneficial implementation scenarios involve a mandatory system for all banned drivers (suspensions & disqualifications), and a mandatory system for banned drivers under the age of 21 only. This latter scenario performs extremely well, with returns up to 16 times the investment with a simple fingerprints-based interlock. Although often more modest, all systems are found to have BCRs above 1 in all our scenarios expect one.

The third edition of the Australian Standard AS1742 Manual of Uniform Traffic Control Devices Part 7 provides a method of calculating the sighting distance required to safely proceed at passive level crossings based on the physics of moving vehicles. This required distance becomes greater with higher line speeds and slower, heavier vehicles so that it may return quite a long sighting distance. However, at such distances, there are also concerns around whether drivers would be able to reliably identify a train in order to make an informed decision regarding whether it would be safe to proceed across the level crossing. In order to determine whether drivers are able to make reliable judgements to proceed in these circumstances, this study assessed the distance at which a train first becomes identifiable to a driver as well as their, ability to detect the movement of the train. A site was selected in Victoria, and 36 participants with good visual acuity observed 4 trains in the 100-140 km/h range. While most participants could detect the train from a very long distance (2.2 km on average), they could only detect that the train was moving at much shorter distances (1.3 km on average). Large variability was observed between participants, with 4 participants consistently detecting trains later than other participants. Participants tended to improve in their capacity to detect the presence of the train with practice, but a similar trend was not observed for detection of the movement of the train. Participants were consistently poor at accurately judging the approach speed of trains, with large underestimations at all investigated distances.

Cyclists are among the most vulnerable road users. Many recent interventions have aimed at improving their safety on the road, such as the minimum overtaking distance rule introduced in Queensland in 2014. Smartphones offer excellent opportunities for technical intervention for road safety at a limited cost. Indeed, they have a lot of available processing power and many embedded sensors that allow analysing a rider's (or driver's) motion, behaviour, and environment; this is especially relevant for cyclists, as they do not have the space or power allowance that can be found in most motor vehicles. The aim of the study presented in this paper is to assess cyclists’ support for a range of new smartphone-based safety technologies. The preliminary results for an online survey with cyclists recruited from Bicycle Queensland and Triathlon Queensland, with N=191, are presented. A number of innovative safety systems such as automatic logging of incidents without injuries, reporting of dangerous area via a website/app, automatic notification of emergency services in case of crash or fall, and advanced navigation apps were assessed. A significant part of the survey is dedicated to GoSafeCycle, a cooperative collision prevention app based on motion tracking and Wi-Fi communications developed at CARRS-Q. Results show a marked preference toward automatic detection and notification of emergencies (62-70% positive assessment) and GoSafeCycle (61.7% positive assessment), as well as reporting apps (59.1% positive assessment). Such findings are important in the context of current promotion of active transports and highlight the need for further development of system supported by the general public.

Due to significant increase in vehicular accident and traffic congestions, vehicle to vehicle (V2V) communication based on the intelligent transport system (ITS) was introduced. However, to carry out efficient design and implementation of a reliable vehicular communication systems,a deep knowledge of the propagation channel characteristics in different environments is crucial, in particular the Doppler and pathloss parameters. Therefore, this paper presents an empirical V2V channel characterization and measurement performed under realistic urban, suburban and highway driving conditions in Brisbane, Australia. Based on Lin Cheng statistical Doppler Model (LCDM), values for the RMS Doppler spread and coherence time due to time selective nature of V2V channels were presented. Also, based on Log-distance power law model, values for the mean pathloss exponent and the standard deviation of shadowing were reported for urban, suburban and highway environments. The V2V channel parameters can be useful to system designers for the purpose of evaluating, simulating and developing new protocols and systems.

IEEE 802.11p is the new standard for intervehicular communications (IVC) using the 5.9 GHz frequency band; it is planned to be widely deployed to enable cooperative systems. 802.11p uses and performance have been studied theoretically and in simulations over the past years. Unfortunately, many of these results have not been confirmed by on-tracks experimentation. In this paper, we describe field trials of 802.11p technology with our test vehicles; metrics such as maximum range, latency and frame loss are examined. Then, we propose a detailed modelisation of 802.11p that can be used to accurately simulate its performance within Cooperative Systems (CS) applications.

Following eco-driving instructions can reduce fuel consumption between 5 to 20% on urban roads with manual cars. The majority of Australian cars have an automatic transmission gear-box. It is therefore of interest to verify whether current eco-driving instructions are e cient for such vehicles. In this pilot study, participants (N=13) drove an instrumented vehicle (Toyota Camry 2007) with an automatic transmission. Fuel consumption of the participants was compared before and after they received simple eco-driving instructions. Participants drove the same vehicle on the same urban route under similar tra c conditions. We found that participants drove at similar speeds during their baseline and eco-friendly drives, and reduced the level of their accelerations and decelerations during eco-driving. Fuel consumption decreased for the complete drive by 7%, but not on the motorway and inclined sections of the study. Gas emissions were estimated with the VT-micro model, and emissions of the studied pollutants (CO2, CO, NOX and HC) were reduced, but no di erence was observed for CO2 on the motorway and inclined sections. The di erence for the complete lap is 3% for CO2. We have found evidence showing that simple eco-driving instructions are e cient in the case of automatic transmission in an urban environment, but towards the lowest values of the spectrum of fuel consumption reduction from the di erent eco-driving studies.

IEEE 802.11p is the new standard for Inter-Vehicular Communications (IVC) using the 5.9 GHz frequency band, as part of the DSRC framework; it will enable applications based on Cooperative Systems. Simulation is widely used to estimate or verify the potential benefits of such cooperative applications, notably in terms of safety for the drivers. We have developed a performance model for 802.11p that can be used by simulations of cooperative applications (e.g. collision avoidance) without requiring intricate models of the whole IVC stack. Instead, it provide a a straightforward yet realistic modelisation of IVC performance. Our model uses data from extensive field trials to infer the correlation between speed, distance and performance metrics such as maximum range, latency and frame loss. Then, we improve this model to limit the number of profiles that have to be generated when there are more than a few couples of emitter-receptor in a given location. Our model generates realistic performance for rural or suburban environments among small groups of IVC-equipped vehicles and road side units.

Cooperative Systems provide, through the multiplication of information sources over the road, a lot of potential to improve the safety of road users, especially drivers. However, developing cooperative ITS applications requires additional resources compared to non-cooperative applications which are both time consuming and expensive. In this paper, we present a simulation architecture aimed at prototyping cooperative ITS applications in an accurate and detailed, close-to-reality environment; the architecture is designed to be modular and generalist. It can be used to simulate any type of CS applications as well as augmented perception. Then, we discuss the results of two applications deployed with our architecture, using a common freeway emergency braking scenario. The first application is Emergency Electronic Brake Light (EEBL); we discuss improvements in safety in terms of the number of crashes and the severity of crashes. The second application compares the performance of a cooperative risk assessment using an augmented map against a non-cooperative approach based on local-perception only. Our results show a systematic improvement of forward warning time for most vehicles in the string when using the augmented-map-based risk assessment.

Cooperative Systems provide, through the multiplication of information sources over the road, a lot of potential to improve the assessment of the road risk describing a particular driving situation. In this paper, we compare the performance of a cooperative risk assessment approach against a non-cooperative approach; we used an advanced simulation framework, allowing for accurate and detailed, close-to-reality simulations. Risk is estimated, in both cases, with combinations of indicators based on the TTC. For the non-cooperative approach, vehicles are equipped only with an AAC-like forward-facing ranging sensor. On the other hand, for the cooperative approach, vehicles share information through 802.11p IVC and create an augmented map representing their environment; risk indicators are then extracted from this map. Our system shows that the cooperative risk assessment provides a systematic increase of forward warning to most of the vehicles involved in a freeway emergency braking scenario, compared to a non-cooperative system.

For a decade, embedded driving assistance systems were mainly dedicated to the management of short time events (lane departure, collision avoidance, collision mitigation). Recently a great number of projects have been focused on cooperative embedded devices in order to extend environment perception. Handling an extended perception range is important in order to provide enough information for both path planning and co-pilot algorithms which need to anticipate events. To carry out such applications, simulation has been widely used. Simulation is efficient to estimate the benefits of Cooperative Systems (CS) based on Inter-Vehicular Communications (IVC). This paper presents a new and modular architecture built with the SiVIC simulator and the RTMaps™ multi-sensors prototyping platform. A set of improvements, implemented in SiVIC, are introduced in order to take into account IVC modelling and vehicles’ control. These 2 aspects have been tuned with on-road measurements to improve the realism of the scenarios. The results obtained from a freeway emergency braking scenario are discussed.

Simulation has been widely used to estimate the benefits of Cooperative Systems (CS) based on Inter-Vehicular Communications (IVC). This paper presents a new architecture built with the SiVIC simulator and the RTMaps™ multisensors prototyping platform. We introduce several improvements from a previous similar architecture, regarding IVC modelisation and vehicles’ control. It has been tuned with on-road measurements to improve fidelity. We discuss the results of a freeway emergency braking scenario (EEBL) implemented to validate our architecture’s capabilities.

IEEE 802.11p is the new standard for inter-vehicular communications (IVC) using the 5.9 GHz frequency band; it is planned to be widely deployed to enable cooperative systems. 802.11p uses and performance have been studied theoretically and in simulations over the past years. Unfortunately, many of these results have not been confirmed by on-tracks experimentation. In this paper, we describe field trials of 802.11p technology with our test vehicles. Metrics such as maximum range, latency and frame loss are examined.

Inter-Vehicular Communications (IVC) are considered a promising technological approach for enhancing transportation safety and improving highway efficiency. Previous theoretical work has demonstrated the benefits of IVC in vehicles strings. Simulations of partially IVC-equipped vehicles strings showed that only a small equipment ratio is sufficient to drastically reduce the number of head on collisions. However, these results are based on the assumptions that IVC exhibit lossless and instantaneous messages transmission. This paper presents the research design of an empirical measurement of a vehicles string, with the goal of highlighting the constraints introduced by the actual characteristics of communication devices. A warning message diffusion system based on IEEE 802.11 wireless technology was developed for an emergency breaking scenario. Preliminary results are presented as well, showing the latencies introduced by using 802.11a and discussing early findings and experimental limitations

On the road, near collision events (also close calls or near-miss incidents) largely outnumber actual crashes, yet most of them can never be recorded by current traffic data collection technologies or crashes analysis tools. The analysis of near collisions data is an important step in the process of reducing the crash rate. There have been several studies that have investigated near collisions; to our knowledge, this is the first study that uses the functionalities provided by cooperative vehicles to collect near misses information. We use the VISSIM traffic simulator and a custom C++ engine to simulate cooperative vehicles and their ability to detect near collision events. Our results showed that, within a simple simulated environment, adequate information on near collision events can be collected using the functionalities of cooperative perception systems. The relationship between the ratio of detected events and the ratio of equipped vehicle was shown to closely follow a squared law, and the largest source of nondetection was packet loss instead of packet delays and GPS imprecision.

A number of advanced driver assistance systems (ADAS) are currently being released on the market, providing safety functions to the drivers such as collision avoidance, adaptive cruise control or enhanced night-vision. These systems however are inherently limited by their sensory range: they cannot gather information from outside this range, also called their “perceptive horizon”. Cooperative systems are a developing research avenue that aims at providing extended safety and comfort functionalities by introducing vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) wireless communications to the road actors. This paper presents the problematic of cooperative systems, their advantages and contributions to road safety and exposes some limitations related to market penetration, sensors accuracy and communications scalability. It explains the issues of how to implement extended perception, a central contribution of cooperative systems. The initial steps of an evaluation of data fusion architectures for extended perception are exposed.

Crash risk is the statistical probability of a crash. Its assessment can be performed through ex post statistical analysis or in real-time with on-vehicle systems. These systems can be cooperative. Cooperative Vehicle-Infrastructure Systems (CVIS) are a developing research avenue in the automotive industry worldwide. This paper provides a survey of existing CVIS systems and methods to assess crash risk with them. It describes the advantages of cooperative systems versus non-cooperative systems. A sample of cooperative crash risk assessment systems is analysed to extract vulnerabilities according to three criteria: market penetration, over-reliance on GPS and broadcasting issues. It shows that cooperative risk assessment systems are still in their infancy and requires further development to provide their full benefits to road users.

There has been increased research interest in Co-operative Vehicle Infrastructure Systems (CVIS) from the eld of Intelligent Transport Systems (ITS). However most of the research have focused on the engineering aspects and overlooked their relevance to the drivers' behaviour. This paper argues that the priority for cooperative systems is the need to improve drivers decision making and reduce drivers' crash risk exposure to improve road safety. Therefore any engineering solutions need to be considered in conjuction with traffic psychology theories on driver behaviour. This paper explores the advantages and limitations of existing systems and emphasizes various theoretical issues that arise in articulating cooperative systems' capabilities and drivers' behaviour.