Browsing by Author "Sullman, Mark"

Now showing 1 - 6 of 6
  • Thumbnail Image
    ItemOpen Access
    Analysis of autopilot disengagements occurring during autonomous vehicle testing
    (IEEE, 2017-12-20) Lv, Chen; Cao, Dongpu; Zhao, Yifan; Auger, Daniel J.; Sullman, Mark; Wang, Huaji; Millen Dutka, Laura; Skrypchuk, Lee; Mouzakitis, Alexandros
    In present-day highly-automated vehicles, there are occasions when the driving system disengages and the human driver is required to take-over. This is of great importance to a vehicle U+02BC s safety and ride comfort. In the U.S state of California, the Autonomous Vehicle Testing Regulations require every manufacturer testing autonomous vehicles on public roads to submit an annual report summarizing the disengagements of the technology experienced during testing. On 1 January 2016, seven manufacturers submitted their first disengagement reports: Bosch, Delphi, Google, Nissan, Mercedes-Benz, Volkswagen, and Tesla Motors. This work analyses the data from these disengagement reports with the aim of gaining abetter understanding of the situations in which a driver is required to takeover, as this is potentially useful in improving the Society of Automotive Engineers U+0028 SAE U+0029 Level 2 and Level 3 automation technologies. Disengagement events from testing are classified into different groups based on attributes and the causes of disengagement are investigated and compared in detail. The mechanisms and time taken for take-over transition occurred in disengagements are studied. Finally, recommendations for OEMs, manufacturers, and government organizations are also discussed.
  • Thumbnail Image
    ItemOpen Access
    Characterization of driver neuromuscular dynamics for human-automation collaboration design of automated vehicles
    (IEEE, 2018-03-05) Lv, Chen; Wang, Huaji; Cao, Dongpu; Zhao, Yifan; Auger, Daniel J.; Sullman, Mark; Matthias, Rebecca; Skrypchuk, Lee; Mouzakitis, Alexandros
    In order to design an advanced human-automation collaboration system for highly automated vehicles, research into the driver's neuromuscular dynamics is needed. In this paper a dynamic model of drivers' neuromuscular interaction with a steering wheel is firstly established. The transfer function and the natural frequency of the systems are analyzed. In order to identify the key parameters of the driver-steering-wheel interacting system and investigate the system properties under different situations, experiments with driver-in-the-loop are carried out. For each test subject, two steering tasks, namely the passive and active steering tasks, are instructed to be completed. Furthermore, during the experiments, subjects manipulated the steering wheel with two distinct postures and three different hand positions. Based on the experimental results, key parameters of the transfer function model are identified by using the Gauss-Newton algorithm. Based on the estimated model with identified parameters, investigation of system properties is then carried out. The characteristics of the driver neuromuscular system are discussed and compared with respect to different steering tasks, hand positions and driver postures. These experimental results with identified system properties provide a good foundation for the development of a haptic take-over control system for automated vehicles.
  • No Thumbnail Available
    ItemOpen Access
    Data for "An Orientation Sensor based Head Tracking System for Driver Behaviour Monitoring"
    (Cranfield University, 2017-11-21 13:42) Zhao, Yifan; Görne, Lorenz; Yuen, Iek-Man; Cao, Dongpu; Sullman, Mark; Auger, Daniel; Lv, Chen; Wang, Huaji; Matthias, Rebecca; Skrypchuk, Lee; Mouzakitis, Alexandros
    Data used for this paper - files created in MATLAB.
  • No Thumbnail Available
    ItemOpen Access
    Data for the paper "Analysis of Autopilot Disengagements Occurring during Autonomous Vehicle Testing"
    (Cranfield University, 2017-12-11 08:19) Lyu, Chen; Cao, Dongpu; Zhao, Yifan; Auger, Daniel; Sullman, Mark; Wang, Huaji
    Data used in the paper "Analysis of Autopilot Disengagements Occurring during Autonomous Vehicle Testing".
  • Thumbnail Image
    ItemOpen Access
    An orientation sensor based head tracking system for driver behaviour monitoring
    (MDPI, 2017-11-22) Zhao, Yifan; Görne, Lorenz; Yuen, Iek-Man; Cao, Dongpu; Sullman, Mark; Auger, Daniel J.; Lv, Chen; Wang, Huaji; Matthias, Rebecca; Skrypchuk, Lee; Mouzakitis, Alexandros
    Although at present legislation does not allow drivers in a Level 3 autonomous vehicle to engage in a secondary task, there may become a time when it does. Monitoring the behaviour of drivers engaging in various non-driving activities (NDAs) is crucial to decide how well the driver will be able to take over control of the vehicle. One limitation of the commonly used face-based head tracking system, using cameras, is that sufficient features of the face must be visible, which limits the detectable angle of head movement and thereby measurable NDAs, unless multiple cameras are used. This paper proposes a novel orientation sensor based head tracking system that includes twin devices, one of which measures the movement of the vehicle while the other measures the absolute movement of the head. Measurement error in the shaking and nodding axes were less than 0.4°, while error in the rolling axis was less than 2°. Comparison with a camera-based system, through in-house tests and on-road tests, showed that the main advantage of the proposed system is the ability to detect angles larger than 20° in the shaking and nodding axes. Finally, a case study demonstrated that the measurement of the shaking and nodding angles, produced from the proposed system, can effectively characterise the drivers’ behaviour while engaged in the NDAs of chatting to a passenger and playing on a smartphone.
  • Thumbnail Image
    ItemOpen Access
    Polish adaptation of the driving and riding avoidance scale
    (De Gruyter Open, 2013-04-12) Błachnio, Agata; Przepiórka, Aneta; Sullman, Mark; Taylor, Joanne
    Driving anxiety is a relatively undervalued topic of research, despite the fact that it can have a substantial detrimental impact on an individuals' life. The prevalence of driving anxiety in motor vehicle crash (MVC) survivors has been found to range from 18-77%. Although driving anxiety can develop without crash involvement, no information currently exists on the prevalence of driving anxiety in the general population. One barrier to gathering this information is that most of the instruments are designed to measure driving anxiety in MVC survivors. However, the Driving and Riding Avoidance Scale (DRAS; Stewart & St. Peter, 2004) is one instrument that shows promise as a more general measure of driving anxiety, although previous research has noted the need for some minor adaptations (Taylor & Sullman, 2009). Therefore, the present study investigated the psychometric properties of an adapted version of the DRAS and the level of driving anxiety amongst a sample of 210 Polish participants. Internal consistency for the overall DRAS was .91 and ranged from .77 to .85 for the subscales. Factor analysis of the DRAS resulted in two clear factors, with the first containing driving avoidance items and the second consisting solely of riding avoidance items. Therefore it appears that the DRAS can be a useful measure of driving avoidance in samples drawn from the general population as well as MVC survivors.