Influence of personal mobile phone ringing and usual intention to answer on driver error

Given evidence of effects of mobile phone use on driving, and also legislation, many careful drivers refrain from answering their phones when driving. However, the distracting influence of a call on driving, even in the context of not answering, has not been examined. Furthermore, given that not answering may be contrary to an individual’s normal habits, this study examined whether distraction caused by the ignored call varies according to normal intention to answer whilst driving. That is, determining whether the effect is more than a simple matter of noise distraction. Participants were 27 young drivers (18-29 years), all regular mobile users. A Theory of Planned Behaviour questionnaire examined predictors of intention to refrain from answering calls whilst driving. Participants provided their mobile phone number and were instructed not to answer their phone if it were to ring during a driving simulation. The simulation scenario had seven hazards (e.g. car pulling out, pedestrian crossing) with three being immediately preceded by a call. Infractions (e.g. pedestrian collisions, vehicle collisions, speed exceedances) were significantly greater when distracted by call tones than with no distraction. Lower intention to ignore calls whilst driving correlated with a larger effect of distraction, as was feeling unable to control whether one answered whilst driving (Perceived Behavioural Control). The study suggests that even an ignored call can cause significantly increased infractions in simulator driving, with pedestrian collisions and speed exceedances being striking examples. Results are discussed in relation to cognitive demands of inhibiting normal behaviour and to drivers being advised to switch phones off whilst driving.

Use of ITS to improve level crossings safety : open issues

Level crossing crashes have been shown to result in enormous human and financial cost to society. According to the Australian Transport Safety Bureau (ATSB) [5] a total of 632 Railway Level crossing (RLX) collisions, between trains and road vehicles, occurred in Australia between 2001 and June 2009. The cost of RLX collisions runs into the tens of millions of dollars each year in Australia [6]. In addition, loss of life and injury are commonplace in instances where collisions occur. Based on estimates that 40% of rail related fatalities occur at level crossings [12], it is estimated that 142 deaths between 2001 and June 2009 occurred at RLX. The aim of this paper is to (i) summarise crash patterns in Australia, (ii) review existing international ITS interventions to improve level crossing and (iii) highlights open human factors research related issues. Human factors (e.g., driver error, lapses or violations) have been evidenced as a significant contributing factor in RLX collisions, with drivers of road vehicles particularly responsible for many collisions. Unintentional errors have been found to contribute to 46% of RLX collisions [6] and appear to be far more commonplace than deliberate violations. Humans have been found to be inherently inadequate at using the sensory information available to them to facilitate safe decision-making at RLX and tend to underestimate the speed of approaching large objects due to the non-linear increases in perceived size [6]. Collisions resulting from misjudgements of train approach speed and distance are common [20]. Thus, a fundamental goal for improved RLX safety is the provision of sufficient contextual information to road vehicle drivers to facilitate safe decision-making regarding crossing behaviours.

Incorporating human-factors in car-following models : a review of recent developments and research needs

Over the past decades there has been a considerable development in the modeling of car-following (CF) behavior as a result of research undertaken by both traffic engineers and traffic psychologists. While traffic engineers seek to understand the behavior of a traffic stream, traffic psychologists seek to describe the human abilities and errors involved in the driving process. This paper provides a comprehensive review of these two research streams. It is necessary to consider human-factors in {CF} modeling for a more realistic representation of {CF} behavior in complex driving situations (for example, in traffic breakdowns, crash-prone situations, and adverse weather conditions) to improve traffic safety and to better understand widely-reported puzzling traffic flow phenomena, such as capacity drop, stop-and-go oscillations, and traffic hysteresis. While there are some excellent reviews of {CF} models available in the literature, none of these specifically focuses on the human factors in these models. This paper addresses this gap by reviewing the available literature with a specific focus on the latest advances in car-following models from both the engineering and human behavior points of view. In so doing, it analyses the benefits and limitations of various models and highlights future research needs in the area.

The nature of errors made by drivers [Austroads Publication no: AP-R378-11]

It is important that we understand the factors and conditions that shape driver behaviour – those conditions within the road transport system that contribute to driver error and the situations where driver non-compliance to road regulations is likely. This report presents the findings derived from a program of research investigating the nature of errors made by drivers, involving a literature review and an on-road study. The review indicates that, despite significant investigation, the role of different error types in road traffic crashes remains unclear, as does the role of the wider road transport system failures in driver error causation.

To err (on the road) is human? An on-road study of driver errors

Human error, its causes and consequences, and the ways in which it can be prevented, remain of great interest to road safety practitioners. This paper presents the findings derived from an on-road study of driver errors in which 25 participants drove a pre-determined route using MUARC's On-Road Test Vehicle (ORTeV). In-vehicle observers recorded the different errors made, and a range of other data was collected, including driver verbal protocols, forward, cockpit and driver video, and vehicle data (speed, braking, steering wheel angle, lane tracking etc). Participants also completed a post trial cognitive task analysis interview. The drivers tested made a range of different errors, with speeding violations, both intentional and unintentional, being the most common. Further more detailed analysis of a sub-set of specific error types indicates that driver errors have various causes, including failures in the wider road 'system' such as poor roadway design, infrastructure failures and unclear road rules. In closing, a range of potential error prevention strategies, including intelligent speed adaptation and road infrastructure design, are discussed.

An Advanced Gate Driver for Insulated Gate Bipolar Transistors to Eliminate Dead-time Induced Distortions in Inverter Output

Dead-time is introduced between the gating signals to the top and bottom switches in a voltage source inverter (VSI) leg, to prevent shoot through fault due to the finite turn-off times of IGBTs. The dead-time results in a delay when the incoming device is an IGBT, resulting in error voltage pulses in the inverter output voltage. This paper presents the design, fabrication and testing of an advanced gate driver, which eliminates dead-time and consequent output distortion. Here, the gating pulses are generated such that the incoming IGBT transition is not delayed and shoot-through is also prevented. The various logic units of the driver card and fault tolerance of the driver are verified through extensive tests on different topologies such as chopper, half-bridge and full-bridge inverter, and also at different conditions of load. Experimental results demonstrate the improvement in the load current waveform quality with the proposed circuit, on account of elimination of dead-time.

Bias-free identification of a linear model-predictive steering controller from measured driver steering behavior

Recent developments in modeling driver steering control with preview are reviewed. While some validation with experimental data has been presented, the rigorous application of formal system identification methods has not yet been attempted. This paper describes a steering controller based on linear model-predictive control. An indirect identification method that minimizes steering angle prediction error is developed. Special attention is given to filtering the prediction error so as to avoid identification bias that arises from the closed-loop operation of the driver-vehicle system. The identification procedure is applied to data collected from 14 test drivers performing double lane change maneuvers in an instrumented vehicle. It is found that the identification procedure successfully finds parameter values for the model that give small prediction errors. The procedure is also able to distinguish between the different steering strategies adopted by the test drivers. © 2006 IEEE.

Linear quadratic game and non-cooperative predictive methods for potential application to modelling driver-AFS interactive steering control

This paper is concerned with the modelling of strategic interactions between the human driver and the vehicle active front steering (AFS) controller in a path-following task where the two controllers hold different target paths. The work is aimed at extending the use of mathematical models in representing driver steering behaviour in complicated driving situations. Two game theoretic approaches, namely linear quadratic game and non-cooperative model predictive control (non-cooperative MPC), are used for developing the driver-AFS interactive steering control model. For each approach, the open-loop Nash steering control solution is derived; the influences of the path-following weights, preview and control horizons, driver time delay and arm neuromuscular system (NMS) dynamics are investigated, and the CPU time consumed is recorded. It is found that the two approaches give identical time histories as well as control gains, while the non-cooperative MPC method uses much less CPU time. Specifically, it is observed that the introduction of weight on the integral of vehicle lateral displacement error helps to eliminate the steady-state path-following error; the increase in preview horizon and NMS natural frequency and the decline in time delay and NMS damping ratio improve the path-following accuracy. © 2013 Copyright Taylor and Francis Group, LLC.

Driver expectancy in highway design and traffic operations. Final report.

Mode of access: Internet.

Tri-level study: modification. Task 5: an examination of driver characteristics and collision producing errors of accident and traffic violation repeaters. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Driver and vehicle characteristics as related to the precipitation of accidents. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Human factors of driver-vehicle interaction in the IVHS environment. Final report.

National Highway Traffic Safety Administration, Washington, D.C.