Effects of Visibility and Alarm Modality on Workload, Trust in Automation, Situation Awareness, and Driver Performance

Driving demands sustained driver attention. This attentional demand increases with decreasing field visibility. In the past researchers have explored and investigated how collision avoidance warning systems (CAWS) help improve driving performance. The goal of the present study is to determine whether auditory or tactile CAWS have a greater effect on driver performance, perceived workload, system trust, and situation awareness (SA). Sixty-three undergraduate students from Old Dominion University participated in this study. Participants were asked to complete two simulated driving sessions along with Motion Sickness Susceptibility Questionnaire, Background Information Questionnaire, Trust Questionnaire, NASA Task Load Index Questionnaire, Situation Awareness Rating Technique Questionnaire, and Simulator Sickness Questionnaire. Analyses indicated that drivers in the tactile modality condition had low perceived workload. Drivers in the heavy fog visibility condition had the highest number of collisions and red-light tickets. Drivers in the heavy fog condition also reported having the highest overall situation awareness. Drivers in the clear visibility condition trusted tactile alarms more than the auditory alarms, whereas drivers in the heavy fog condition trusted auditory alarms more than tactile alarms. The findings of this investigation could be applied to improve the design of CAWS that would help improve driver performance and increase safety on the roadways

Safe Local Navigation for Visually Impaired Users With a Time-of-Flight and Haptic Feedback Device

This paper presents ALVU (Array of Lidars and Vibrotactile Units), a contactless, intuitive, hands-free, and discreet wearable device that allows visually impaired users to detect low- and high-hanging obstacles, as well as physical boundaries in their immediate environment. The solution allows for safe local navigation in both confined and open spaces by enabling the user to distinguish free space from obstacles. The device presented is composed of two parts: a sensor belt and a haptic strap. The sensor belt is an array of time-of-flight distance sensors worn around the front of a user's waist, and the pulses of infrared light provide reliable and accurate measurements of the distances between the user and surrounding obstacles or surfaces. The haptic strap communicates the measured distances through an array of vibratory motors worn around the user's upper abdomen, providing haptic feedback. The linear vibration motors are combined with a point-loaded pretensioned applicator to transmit isolated vibrations to the user. We validated the device's capability in an extensive user study entailing 162 trials with 12 blind users. Users wearing the device successfully walked through hallways, avoided obstacles, and detected staircases.Andrea Bocelli FoundationNational Science Foundation (U.S.) (Grant NSF IIS1226883

The Human Factors of Transitions in Highly Automated Driving

The aim of this research was to investigate the nature of the out-of-the-loop (OoTL) phenomenon in highly automated driving (HAD), and its effect on driver behaviour before, during, and after the transition from automated to manual control. The work addressed questions relating to how automation affects drivers' (i) performance in transition situations requiring control- and tactical-level responses, (ii) their behaviour in automation compared to in manual driving, (iii-iv) their visual attention distribution before and during the transition, as well as (v) their perceptual-motor performance after resuming control. A series of experiments were developed to take drivers progressively further OoTL for short periods during HAD, by varying drivers' secondary task engagement and the amount of visual information from the system and environment available to them. Once the manipulations ended, drivers were invited to determine a need to resume control in critical and non-critical vehicle following situations. Results showed that, overall, drivers looked around more during HAD, compared to manual driving, and had poorer vehicle control in critical transition situations. Generally, the further OoTL drivers were during HAD, the more dispersed their visual attention. However, within three seconds of the manipulations ending, the differences between the conditions resolved, and in many cases, this was before drivers resumed control. Differences between the OoTL manipulations emerged once again in terms of the timing of drivers' initial response (take-over time) in critical events, where the further OoTL drivers were the longer it took them to resume control, but there was no difference in the quality of the subsequent vehicle control. Results suggest that any information presented to drivers during automation should be placed near the centre of the road and that kinematically early avoidance response may be more important for safety than short take-over times. This thesis concludes with a general conceptualisation of the relationship between a number of driver and vehicle/environment factors that influence driver performance in the transition

Effects of modality, urgency and situation on responses to multimodal warnings for drivers

Signifying road-related events with warnings can be highly beneficial, especially when imminent attention is needed. This thesis describes how modality, urgency and situation can influence driver responses to multimodal displays used as warnings. These displays utilise all combinations of audio, visual and tactile modalities, reflecting different urgency levels. In this way, a new rich set of cues is designed, conveying information multimodally, to enhance reactions during driving, which is a highly visual task. The importance of the signified events to driving is reflected in the warnings, and safety-critical or non-critical situations are communicated through the cues. Novel warning designs are considered, using both abstract displays, with no semantic association to the signified event, and language-based ones, using speech. These two cue designs are compared, to discover their strengths and weaknesses as car alerts. The situations in which the new cues are delivered are varied, by simulating both critical and non-critical events and both manual and autonomous car scenarios. A novel set of guidelines for using multimodal driver displays is finally provided, considering the modalities utilised, the urgency signified, and the situation simulated

Toward Computational Simulations of Behavior During Automated Driving Takeovers: A Review of the Empirical and Modeling Literatures

Objective: This article provides a review of empirical studies of automated vehicle takeovers and driver modeling to identify influential factors and their impacts on takeover performance and suggest driver models that can capture them. Background: Significant safety issues remain in automated-to-manual transitions of vehicle control. Developing models and computer simulations of automated vehicle control transitions may help designers mitigate these issues, but only if accurate models are used. Selecting accurate models requires estimating the impact of factors that influence takeovers. Method: Articles describing automated vehicle takeovers or driver modeling research were identified through a systematic approach. Inclusion criteria were used to identify relevant studies and models of braking, steering, and the complete takeover process for further review. Results: The reviewed studies on automated vehicle takeovers identified several factors that significantly influence takeover time and post-takeover control. Drivers were found to respond similarly between manual emergencies and automated takeovers, albeit with a delay. The findings suggest that existing braking and steering models for manual driving may be applicable to modeling automated vehicle takeovers. Conclusion: Time budget, repeated exposure to takeovers, silent failures, and handheld secondary tasks significantly influence takeover time. These factors in addition to takeover request modality, driving environment, non-handheld secondary tasks, level of automation, trust, fatigue, and alcohol significantly impact post-takeover control. Models that capture these effects through evidence accumulation were identified as promising directions for future work. Application: Stakeholders interested in driver behavior during automated vehicle takeovers may use this article to identify starting points for their work

License to Supervise:Influence of Driving Automation on Driver Licensing

To use highly automated vehicles while a driver remains responsible for safe driving, places new – yet demanding, requirements on the human operator. This is because the automation creates a gap between drivers’ responsibility and the human capabilities to take responsibility, especially for unexpected or time-critical transitions of control. This gap is not being addressed by current practises of driver licensing. Based on literature review, this research collects drivers’ requirements to enable safe transitions in control attuned to human capabilities. This knowledge is intended to help system developers and authorities to identify the requirements on human operators to (re)take responsibility for safe driving after automation

Development of a workload estimator: The influence of surrounding traffic behaviour on driver workload and performance

The consumers’ increasing desire to be connected at all times and the advancement of integrated functionality within the vehicle, increases the risk that drivers could be faced with information overload while driving. Given the importance of human interaction with technology within the vehicle, automobile manufacturers are introducing workload manager systems within the vehicles to help prevent driver overload. However the ability of the system to decide in a timely manner requires anticipation of changes in workload, depending on the capacity of the driver and matching it with the demand expected from the driving task such as the dynamic traffic environment. In relation to the need to understand the influence of traffic demand on driver workload, the work here comprises the systematic manipulation of traffic complexity and exploration of workload measures to highlight which are sensitive to primary task demand manipulated. A within-subjects design was used in the studies explored in this thesis to allow comparison between different manipulated traffic conditions. In the first simulator test, the ability of various objective and subjective workload measures to tap into drivers’ momentary workload was examined. Following the identification of a subjective measure that was sensitive to the influence of lane changes performed by neighbouring vehicle on drivers’ momentary workload, the characteristics of the lane change were explored in the subsequent studies involving single and dual-task conditions. Overall, these studies suggested suppression of non-urgent communications by a workload manager during safety-critical conditions involving critical cut-ins would be advantageous to both younger and older drivers. This thesis offers a novel and valuable contribution to the design of a workload estimator so as to ensure that the driving demand is always within drivers’ capacity to avoid driver overload. Results of these studies have also highlighted the utility of vehicle-based sensor data in improving workload manager functionality