scholarly journals Take-over expectation and criticality in Level 3 automated driving: a test track study on take-over behavior in semi-trucks

2020 ◽  
Vol 22 (4) ◽  
pp. 733-744
Author(s):  
Alexander Lotz ◽  
Nele Russwinkel ◽  
Enrico Wohlfarth
Keyword(s):  
High Speed ◽  
Driver Behavior ◽  
Reaction Times ◽  
Multiple Time ◽  
Automated Driving ◽  
Passenger Cars ◽  
Test Track ◽  
Driving Assistance ◽  
Level 3 ◽  
Critical Situations

Abstract With the introduction of advanced driving assistance systems managing longitudinal and lateral control, conditional automated driving is seemingly in near future of series vehicles. While take-over behavior in the passenger car context has been investigated intensively in recent years, publications on semi-trucks with professional drivers are sparse. The effects influencing expert drivers during take-overs in this context lack thorough investigation and are required to design systems that facilitate safe take-overs. While multiple findings seem to cohere in passenger cars and semi-trucks, these findings rely on simulated studies without taking environments as found in the real world into account. A test track study was conducted, simulating highway driving with 27 professional non-affiliated truck drivers. The participants drove an automated Level 3 semi-truck while a non-driving-related task was available. Multiple time critical take-over situations were initiated during the drives to investigate four main objectives regarding driver behavior. (1) With these results, comparison of reaction times and behavior can be drawn to previous simulator studies. The effect of situation criticality (2) and training (3) of take-over situations is investigated. (4) The influence of warning expectation on driver behavior is explored. Results obtained displayed very quick time to hands on steering and time to first reaction all under 2.4 s. Highly critical situations generate very quick reaction times M = 0.81 s, while the manipulation of expectancy yielded no significant variation in reaction times. These reaction times serve as a reference of what can be expected from drivers under optimal take-over conditions, with quick reactions at high speed in critical situations.

Evaluation of Imminent Take-Over Requests With Real Automation on a Test Track

2021 ◽  
pp. 001872082110514
Author(s):  
Philipp Wintersberger ◽  
Clemens Schartmüller ◽  
Shadan Shadeghian-Borojeni ◽  
Anna-Katharina Frison ◽  
Andreas Riener
Keyword(s):  
Cognitive Load ◽  
Situation Awareness ◽  
Reaction Times ◽  
Gaze Behavior ◽  
Automated Driving ◽  
Test Track ◽  
Handheld Device ◽  
Critical Transitions ◽  
Real Risk ◽  
Task Modality

Objective Investigating take-over, driving, non-driving related task (NDRT) performance, and trust of conditionally automated vehicles (AVs) in critical transitions on a test track. Background Most experimental results addressing driver take-over were obtained in simulators. The presented experiment aimed at validating relevant findings while uncovering potential effects of motion cues and real risk. Method Twenty-two participants responded to four critical transitions on a test track. Non-driving related task modality (reading on a handheld device vs. auditory) and take-over timing (cognitive load) were varied on two levels. We evaluated take-over and NDRT performance as well as gaze behavior. Further, trust and workload were assessed with scales and interviews. Results Reaction times were significantly faster than in simulator studies. Further, reaction times were only barely affected by varying visual, physical, or cognitive load. Post-take-over control was significantly degraded with the handheld device. Experiencing the system reduced participants’ distrust, and distrusting participants monitored the system longer and more frequently. NDRTs on a handheld device resulted in more safety-critical situations. Conclusion The results confirm that take-over performance is mainly influenced by visual-cognitive load, while physical load did not significantly affect responses. Future take-over request (TOR) studies may investigate situation awareness and post-take-over control rather than reaction times only. Trust and distrust can be considered as different dimensions in AV research. Application Conditionally AVs should offer dedicated interfaces for NDRTs to provide an alternative to using nomadic devices. These interfaces should be designed in a way to maintain drivers’ situation awareness. Précis This paper presents a test track experiment addressing conditionally automated driving systems. Twenty-two participants responded to critical TORs, where we varied NDRT modality and take-over timing. In addition, we assessed trust and workload with standardized scales and interviews.

The Sooner the Better: Drivers’ Reactions to Two-Step Take-Over Requests in Highly Automated Driving

2018 ◽  
Vol 62 (1) ◽  
pp. 1883-1887 ◽  
Author(s):  
Sandra Epple ◽  
Fabienne Roche ◽  
Stefan Brandenburg
Keyword(s):  
Human Factors ◽  
Situational Awareness ◽  
Driver Behavior ◽  
Reaction Times ◽  
Vehicle Control ◽  
Driving Behavior ◽  
Second Step ◽  
Automated Driving ◽  
One Step ◽  
Highly Automated Driving

Driving behavior after take-over requests (TORs) is one of the most popular subjects in human factors re-search on highly automated driving. Many studies utilized one-step TOR procedures to prompt drivers to resume vehicle control. The present paper examines driver behavior when experiencing a two-step TOR procedure in different modalities. A two-step TOR gives drivers a choice to resume vehicle controls be-tween a warning (first step) and an alarm (second step). Our findings indicate that a substantial number of drivers resumes vehicle controls after the second step, resulting in a higher number of crashes. More generally, criticality of the driving situation increases with increasing reaction times. Driving and interview data suggest that step two of the TOR should be presented earlier. Alternatively, a multi-step TOR could be used to increase drivers’ situational awareness. Auditory TORs are associated with shorter reaction times than visual-auditory TORs. Implications on TOR design are discussed.

scholarly journals Effects of non-driving related tasks on mental workload and take-over times during conditional automated driving

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Andreas Lars Müller ◽  
Natacha Fernandes-Estrela ◽  
Ruben Hetfleisch ◽  
Lukas Zecha ◽  
Bettina Abendroth
Keyword(s):  
Driving Simulator ◽  
Mental Workload ◽  
Reaction Times ◽  
Vehicle Control ◽  
Automated Driving ◽  
Level Of Automation ◽  
Spare Time ◽  
High Workload ◽  
Level 3 ◽  
Perceived Workload

Abstract Background Automated driving will be of high value in the future. While in partial-automated driving the driver must always monitor the traffic situation, a paradigm shift is taking place in the case of conditional automated driving (Level 3 according to SAE). From this level of automation onwards, the vehicle user is released from permanent vehicle control and environmental monitoring and is allowed to engage in Non-Driving Related Tasks (NDRT) in his or her newly gained spare time. These tasks can be performed until a take-over request informs the user to resume vehicle control. As the driver is still considered to be the fall-back level, this aspect of taking over control is considered especially critical. Methods While previous research projects have focused their studies on the factors influencing the take-over request, this paper focuses on the effects of NDRT on the user of the vehicle during conditional automated driving, especially on the human workload. NDRT (such as Reading, Listening, Watching a movie, Texting and Monitoring ride) were examined within a static driving simulator at the Institute of Ergonomics & Human Factors with 56 participants in an urban environment. These NDRT were tested for mental workload and the ability to take over in a critical situation. To determine the perceived workload, the subjective workload, psychophysiological activity as well as performance-based parameters of a secondary competing task performed by a were used. Results This study revealed that the selected NDRT vary significantly in their mental workload and that the workload correlates with the length of the time needed for take over control. NDRT which are associated with a high workload (such as Reading or Texting) also lead to longer reaction times.

First encounter effects in testing of highly automated vehicles during two experimental occasions – The need for recurrent testing

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jonas Andersson ◽  
Azra Habibovic ◽  
Daban Rizgary
Keyword(s):  
Driver Behavior ◽  
Added Value ◽  
Future Research ◽  
Gaze Behavior ◽  
Repeated Testing ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Perceived Safety ◽  
Repeated Interaction ◽  
Over Time

Abstract To explore driver behavior in highly automated vehicles (HAVs), independent researchers are mainly conducting short experiments. This limits the ability to explore drivers’ behavioral changes over time, which is crucial when research has the intention to reveal human behavior beyond the first-time use. The current paper shows the methodological importance of repeated testing in experience and behavior related studies of HAVs. The study combined quantitative and qualitative data to capture effects of repeated interaction between drivers and HAVs. Each driver ( n = 8 n=8 ) participated in the experiment on two different occasions (∼90 minutes) with one-week interval. On both occasions, the drivers traveled approximately 40 km on a rural road at AstaZero proving grounds in Sweden and encountered various traffic situations. The participants could use automated driving (SAE level 4) or choose to drive manually. Examples of data collected include gaze behavior, perceived safety, as well as interviews and questionnaires capturing general impressions, trust and acceptance. The analysis shows that habituation effects were attenuated over time. The drivers went from being exhilarated on the first occasion, to a more neutral behavior on the second occasion. Furthermore, there were smaller variations in drivers’ self-assessed perceived safety on the second occasion, and drivers were faster to engage in non-driving related activities and become relaxed (e. g., they spent more time glancing off road and could focus more on non-driving related activities such as reading). These findings suggest that exposing drivers to HAVs on two (or more) successive occasions may provide more informative and realistic insights into driver behavior and experience as compared to only one occasion. Repeating an experiment on several occasions is of course a balance between the cost and added value, and future research should investigate in more detail which studies need to be repeated on several occasions and to what extent.

scholarly journals Detection of tactile inputs in the rat vibrissa pathway

2012 ◽  
Vol 108 (2) ◽  
pp. 479-490 ◽  
Author(s):  
Douglas R. Ollerenshaw ◽  
Bilal A. Bari ◽  
Daniel C. Millard ◽  
Lauren E. Orr ◽  
Qi Wang ◽  
...  
Keyword(s):  
High Speed ◽  
Barrel Cortex ◽  
Sensory Information ◽  
Reaction Times ◽  
Response Probability ◽  
Relevant Information ◽  
Relative Strength ◽  
Spatiotemporal Representation ◽  
Induced Motion ◽  
Electrophysiological Findings

The rapid detection of sensory inputs is crucial for survival. Sensory detection explicitly requires the integration of incoming sensory information and the ability to distinguish between relevant information and ongoing neural activity. In this study, head-fixed rats were trained to detect the presence of a brief deflection of their whiskers resulting from a focused puff of air. The animals showed a monotonic increase in response probability and a decrease in reaction time with increased stimulus strength. High-speed video analysis of whisker motion revealed that animals were more likely to detect the stimulus during periods of reduced self-induced motion of the whiskers, thereby allowing the stimulus-induced whisker motion to exceed the ongoing noise. In parallel, we used voltage-sensitive dye (VSD) imaging of barrel cortex in anesthetized rats receiving the same stimulus set as those in the behavioral portion of this study to assess candidate codes that make use of the full spatiotemporal representation and to compare variability in the trial-by-trial nature of the cortical response and the corresponding variability in the behavioral response. By application of an accumulating evidence framework to the population cortical activity measured in separate animals, a strong correspondence was made between the behavioral output and the neural signaling, in terms of both the response probabilities and the reaction times. Taken together, the results here provide evidence for detection performance that is strongly reliant on the relative strength of signal versus noise, with strong correspondence between behavior and parallel electrophysiological findings.

scholarly journals Supervision of a self-driving vehicle unmasks latent sleepiness relative to manually controlled driving

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Erin E. Flynn-Evans ◽  
Lily R. Wong ◽  
Yukiyo Kuriyagawa ◽  
Nikhil Gowda ◽  
Patrick F. Cravalho ◽  
...  
Keyword(s):  
Human Error ◽  
Reaction Times ◽  
Modern Society ◽  
Accident Risk ◽  
Road Accidents ◽  
Automated Driving ◽  
High Prevalence ◽  
Driving Task ◽  
Sleep Deficiency ◽  
Monitoring Performance

AbstractHuman error has been implicated as a causal factor in a large proportion of road accidents. Automated driving systems purport to mitigate this risk, but self-driving systems that allow a driver to entirely disengage from the driving task also require the driver to monitor the environment and take control when necessary. Given that sleep loss impairs monitoring performance and there is a high prevalence of sleep deficiency in modern society, we hypothesized that supervising a self-driving vehicle would unmask latent sleepiness compared to manually controlled driving among individuals following their typical sleep schedules. We found that participants felt sleepier, had more involuntary transitions to sleep, had slower reaction times and more attentional failures, and showed substantial modifications in brain synchronization during and following an autonomous drive compared to a manually controlled drive. Our findings suggest that the introduction of partial self-driving capabilities in vehicles has the potential to paradoxically increase accident risk.

scholarly journals Built-in-self-test system for FPGA-based vehicle video-detection and distance measurement

2021 ◽  
Author(s):  
Md Forhad Ebn Anwar
Keyword(s):  
High Speed ◽  
Vision System ◽  
Test System ◽  
Distance Measurement ◽  
Assistance System ◽  
Project Work ◽  
Practical Calculation ◽  
Automatic Driving ◽  
Driving Assistance ◽  
Driving Assistance System

Collision of vehicles in highways are very frequent. Because of high speed (more than 100 km/hour), the momentum of collision is too high that leads sever casualty. Automatic Driving Assistance system can assist the vehicle operators to take decision based on realistic practical calculation on safety measures. It is always better to have third eye working parallel with human to avoid road accident. There are several technologies used to develop perfect driving assistance system to achieve higher accuracy in detection, identification and distance measurement of obstacles where vision based system is one of them. Mono-vision system provides cheap and fast solution rather stereo vision. This project work conducted with objective to comprehend computational complexity in implementation of mono-vison camera based object detection where system will generate warning if the detected object has a motion towards target. Processing and analyzing of captured video image is the focused mechanism of implementation and used internal image generator module to mimic actual video camera. Appeared size of the shape of object considered for the decision making. The simulated image pattern can change it’s dimension to represent vehicle movement in one direction (Back and forth). In this work the on-chip car image generation sub-system was proposed designed and partially implemented on the base of the FPGA where Xilinx Zynq-7010 (ZYNQ XC7Z010-1CLG400C) FPGA development board used. Keyword: Computer Vision, mono vision, image processing on FPGA, Automatic Driving Assistance, Vehicle Detection.

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