Using a Vibrotactile Seat for Facilitating the Handover of Control during Automated Driving

2017 ◽  
Vol 9 (3) ◽  
pp. 17-33 ◽  
Author(s):  
Ariel Telpaz ◽  
Brian Rhindress ◽  
Ido Zelman ◽  
Omer Tsimhoni
Keyword(s):  
Eye Tracking ◽  
Spatial Information ◽  
Automated Driving ◽  
Lane Changing ◽  
Driving Mode ◽  
The Road ◽  
Driving Task ◽  
Vibrotactile Display

Studies have found that drivers tend to neglect their surrounding traffic during automated driving. This may lead to a late and inefficient resumption of control in case of handover of the driving task to the driver. The authors evaluated the effectiveness of a vibrotactile seat displaying spatial information regarding vehicles approaching from behind to enhance the driver preparedness to the handover of control. A simulator experiment, involving 26 participants, showed that when drivers were required to regain control of the vehicle, having a vibrotactile seat improved speed and efficiency of reactions in scenarios requiring lane changing immediately following a handover. In addition, eye-tracking analysis showed that the participants had more systematic scan patterns of the mirrors in the first two seconds following the transition of control request. Interestingly, this effect exists in-spite of the finding that during automated driving mode, having a vibrotactile display led to fewer glances at the road.

Off-Road Glance Behavior, Response Time to a Forward Collision Hazard, and Engagement Strategy Effects in Automated Driving

2017 ◽  
Vol 61 (1) ◽  
pp. 1909-1913 ◽  
Author(s):  
Mike Blommer ◽  
Reates Curry ◽  
Dev Kochhar ◽  
Rads Swaminathan ◽  
Walter Talamonti ◽  
...  
Keyword(s):  
Response Time ◽  
Automated Driving ◽  
Secondary Tasks ◽  
Collision Warning ◽  
Driving Mode ◽  
Driver Response ◽  
The Road ◽  
Behavior Response ◽  
The Face ◽  
Forward Collision Warning

Blommer et al. (2015) reported on a simulator study that investigated a driver engagement (DE) strategy designed to keep the driver-in-the-loop during automated driving in the face of two different types of secondary tasks. The method, first reported by Carsten et al. (2012), involved driving in fully automated driving mode for 6 minutes followed by 1 minute of manual driving, after which this fixed schedule was repeated several times throughout the drive. This scheduled strategy was compared to a reference condition in which different participants experienced continuous automated driving without interruptions. For each condition, some participants watched a video and others listened to the radio. All drives ended in automated driving mode with a surprise forward collision (FC) hazard to which the participant had to manually intervene. Compared to video watchers, radio listeners responded faster, looked to the road scene more, and they were more often looking forward at FC event onset. The DE strategy had no effect on radio listeners. In contrast, video watchers responded to the hazard more quickly with the scheduled strategy than without it. However, there was no reliable statistical difference between DE conditions in percent-eye-glance-time looking to the forward road scene during automated driving or in the number of drivers looking forward at FC event onset. This paper presents additional analyses of off-road eye glance behavior and finds no relationship between how long people were looking away prior to receiving a Forward Collision Warning (FCW) and driver response time (RT). About 95% of all video watching drivers glanced back to the road within 20 sec regardless of the automated driving condition. Approximately 85% of glances away from the road in the scheduled mitigation condition were 7 sec or less.

scholarly journals Transitions Between Highly Automated and Longitudinally Assisted Driving: The Role of the Initiator in the Fight for Authority

2020 ◽  
pp. 001872082094618 ◽  
Author(s):  
Davide Maggi ◽  
Richard Romano ◽  
Oliver Carsten
Keyword(s):  
Road Safety ◽  
Driving Simulator ◽  
Cruise Control ◽  
Automated Driving ◽  
Safety Issues ◽  
The Road ◽  
Operational Design ◽  
Level 3 ◽  
Driving Task

Objective A driving simulator study explored how drivers behaved depending on their initial role during transitions between highly automated driving (HAD) and longitudinally assisted driving (via adaptive cruise control). Background During HAD, drivers might issue a take-over request (TOR), initiating a transition of control that was not planned. Understanding how drivers behave in this situation and, ultimately, the implications on road safety is of paramount importance. Method Sixteen participants were recruited for this study and performed transitions of control between HAD and longitudinally assisted driving in a driving simulator. While comparing how drivers behaved depending on whether or not they were the initiators, different handover strategies were presented to analyze how drivers adapted to variations in the authority level they were granted at various stages of the transitions. Results Whenever they initiated the transition, drivers were more engaged with the driving task and less prone to follow the guidance of the proposed strategies. Moreover, initiating a transition and having the highest authority share during the handover made the drivers more engaged with the driving task and attentive toward the road. Conclusion Handover strategies that retained a larger authority share were more effective whenever the automation initiated the transition. Under driver-initiated transitions, reducing drivers’ authority was detrimental for both performance and comfort. Application As the operational design domain of automated vehicles (Society of Automotive Engineers [SAE] Level 3/4) expands, the drivers might very well fight boredom by taking over spontaneously, introducing safety issues so far not considered but nevertheless very important.

scholarly journals From the Concept of Being “the Boss” to the Idea of Being “a Team”: The Adaptive Co-Pilot as the Enabler for a New Cooperative Framework

2021 ◽  
Vol 11 (15) ◽  
pp. 6950
Author(s):  
Mauricio Marcano ◽  
Fabio Tango ◽  
Joseba Sarabia ◽  
Andrea Castellano ◽  
Joshué Pérez ◽  
...  
Keyword(s):  
Shared Control ◽  
Use Case ◽  
System State ◽  
Automated Driving ◽  
Artificial System ◽  
Driving Mode ◽  
Trust In Automation ◽  
Driving Task ◽  
Lane Keeping ◽  
Available Resources

The “classical” SAE LoA for automated driving can present several drawbacks, and the SAE-L2 and SAE-L3, in particular, can lead to the so-called “irony of automation”, where the driver is substituted by the artificial system, but is still regarded as a “supervisor” or as a “fallback mechanism”. To overcome this problem, while taking advantage of the latest technology, we regard both human and machine as members of a unique team that share the driving task. Depending on the available resources (in terms of driver’s status, system state, and environment conditions) and considering that they are very dynamic, an adaptive assignment of authority for each member of the team is needed. This is achieved by designing a technology enabler, constituted by the intelligent and adaptive co-pilot. It comprises (1) a lateral shared controller based on NMPC, which applies the authority, (2) an arbitration module based on FIS, which calculates the authority, and (3) a visual HMI, as an enabler of trust in automation decisions and actions. The benefits of such a system are shown in this paper through a comparison of the shared control driving mode, with manual driving (as a baseline) and lane-keeping and lane-centering (as two commercial ADAS). Tests are performed in a use case where support for a distracted driver is given. Quantitative and qualitative results confirm the hypothesis that shared control offers the best balance between performance, safety, and comfort during the driving task.

Fatigue and Automation-Induced Impairments in Simulated Driving Performance

1998 ◽  
Vol 1628 (1) ◽  
pp. 8-14 ◽  
Author(s):  
Paula A. Desmond ◽  
Peter A. Hancock ◽  
Janelle L. Monette
Keyword(s):  
Intelligent Transportation Systems ◽  
Driving Simulator ◽  
Transportation Systems ◽  
Task Demands ◽  
Manual Control ◽  
Automated Driving ◽  
The Road ◽  
Full Control ◽  
Performance Recovery ◽  
Driving Task

A driving simulator study investigated the effect of automation of the driving task on performance under fatiguing driving conditions. In the study, drivers performed both a manual drive, in which they had full control over the driving task, and an automated drive, in which the vehicle was controlled by an automated driving system. During both drives, three perturbing events occurred at early, intermediate, and late phases in the drives: in the automated drive, a failure in automation caused the vehicle to drift toward the edge of the road; in the manual drive, wind gusts resulted in the vehicle drifting in the same direction and magnitude as the “drifts” in the automated drive. Following automation failure, drivers were forced to control the vehicle manually until the system became operational again. Drivers’ lateral control of the vehicle was assessed during three phases of manual control in both drives. The results indicate that performance recovery was better when drivers had full manual control of the vehicle throughout the drive, rather than when drivers were forced to drive manually following automation failure. Drivers also experienced increased tiredness, and physical and perceptual fatigue symptoms following both drives. The findings have important implications for the design of intelligent transportation systems. Systems that reduce the driver’s perceptions of task demands of driving are likely to undermobilize effort in fatigued drivers. Thus, the results strongly support the contention that human-centered transportation strategies, in which the driver is involved in the driving task, are superior to total automation.

Safety Enhancement by Automated Driving: What are the Relevant Scenarios?

2020 ◽  
Vol 64 (1) ◽  
pp. 1686-1690
Author(s):  
Niklas Grabbe ◽  
Michael Höcher ◽  
Alexander Thanos ◽  
Klaus Bengler
Keyword(s):  
Traffic Safety ◽  
Road Traffic ◽  
Test Cases ◽  
Automated Driving ◽  
Road Traffic Safety ◽  
Human Driver ◽  
Driving Task ◽  
Promising Solution ◽  
Vehicle Automation ◽  
Safety Enhancement

Automated driving offers great possibilities in traffic safety advancement. However, evidence of safety cannot be provided by current validation methods. One promising solution to overcome the approval trap (Winner, 2015) could be the scenario-based approach. Unfortunately, this approach still results in a huge number of test cases. One possible way out is to show the current, incorrect path in the argumentation and strategy of vehicle automation, and focus on the systemic mechanisms of road traffic safety. This paper therefore argues the case for defining relevant scenarios and analysing them systemically in order to ultimately reduce the test cases. The relevant scenarios are based on the strengths and weaknesses, in terms of the driving task, for both the human driver and automation. Finally, scenarios as criteria for exclusion are being proposed in order to systemically assess the contribution of the human driver and automation to road safety.

Assessing the Effect of Countdown Featured TOR Signal on Drivers in Automated Driving Mode Change

2020 ◽  
Vol 64 (1) ◽  
pp. 1213-1217
Author(s):  
HyunJoo Park ◽  
HyunJae Park ◽  
Sang-Hwan Kim
Keyword(s):  
Reaction Time ◽  
Situation Awareness ◽  
Time Preference ◽  
Automated Driving ◽  
Mode Change ◽  
Driving Mode

In conditional automated driving, drivers may be required starting manual driving from automated driving mode after take-over request (TOR). The objective of the study was to investigate different TOR features for drivers to engage in manual driving effectively in terms of reaction time, preference, and situation awareness (SA). Five TOR features, including four features using countdown, were designed and evaluated, consisted of combinations of different modalities and codes. Results revealed the use of non-verbal sound cue (beep) yielded shorter reaction time while participants preferred verbal sound cue (speech). Drivers' SA was not different for TOR features, but the level of SA was affected by different aspects of SA. The results may provide insights into designing multimodal TOR along with drivers' behavior during take-over tasks.

scholarly journals Driver intervention performance assessment as a key aspect of L3–L4 automated vehicles deployment

2021 ◽  
Vol 11 (1) ◽  
pp. 845-852
Author(s):  
Aleksandra Rodak ◽  
Paweł Budziszewski ◽  
Małgorzata Pędzierska ◽  
Mikołaj Kruszewski
Keyword(s):  
Performance Assessment ◽  
Minimal Risk ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Driving System ◽  
Driving Task ◽  
Driving Conditions

Abstract In L3–L4 vehicles, driving task is performed primarily by automated driving system (ADS). Automation mode permits to engage in non-driving-related tasks; however, it necessitates continuous vigilance and attention. Although the driver may be distracted, a request to intervene may suddenly occur, requiring immediate and appropriate response to driving conditions. To increase safety, automated vehicles should be equipped with a Driver Intervention Performance Assessment module (DIPA), ensuring that the driver is able to take the control of the vehicle and maintain it safely. Otherwise, ADS should regain control from the driver and perform a minimal risk manoeuvre. The paper explains the essence of DIPA, indicates possible measures, and describes a concept of DIPA framework being developed in the project.

scholarly journals Platoon Merging Approach Based on Hybrid Trajectory Planning and CACC Strategies

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2626
Author(s):  
Carlos Hidalgo ◽  
Ray Lattarulo ◽  
Carlos Flores ◽  
Joshué Pérez Rastelli
Keyword(s):  
Trajectory Planning ◽  
Traffic Congestion ◽  
Efficient Solutions ◽  
Test Case ◽  
Cruise Control ◽  
Automated Driving ◽  
Modular Architecture ◽  
The Road ◽  
Cooperative Adaptive Cruise Control ◽  
Multiple Vehicles

Currently, the increase of transport demands along with the limited capacity of the road network have increased traffic congestion in urban and highway scenarios. Technologies such as Cooperative Adaptive Cruise Control (CACC) emerge as efficient solutions. However, a higher level of cooperation among multiple vehicle platoons is needed to improve, effectively, the traffic flow. In this paper, a global solution to merge two platoons is presented. This approach combines: (i) a longitudinal controller based on a feed-back/feed-forward architecture focusing on providing CACC capacities and (ii) hybrid trajectory planning to merge platooning on straight paths. Experiments were performed using Tecnalia’s previous basis. These are the AUDRIC modular architecture for automated driving and the highly reliable simulation environment DYNACAR. A simulation test case was conducted using five vehicles, two of them executing the merging and three opening the gap to the upcoming vehicles. The results showed the good performance of both domains, longitudinal and lateral, merging multiple vehicles while ensuring safety and comfort and without propagating speed changes.

Analysis of Driver's Characteristics Based on Internet of Vehicles

2014 ◽  
Vol 505-506 ◽  
pp. 1148-1152
Author(s):  
Jian Qun Wang ◽  
Xiao Qing Xue ◽  
Ning Cao
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
Classification Model ◽  
Economic Losses ◽  
Lane Changing ◽  
Internet Of Vehicles ◽  
Factors Affecting ◽  
The Road ◽  
The University ◽  
Driver’S Characteristics

The road traffic accidents caused huge economic losses and casualties, so it had been focused by the researchers. Lane changing characteristic is the most relevant characteristic with safety. The intent of lane changing was discussed. Firstly, the factors affecting the intent were analyzed, the speed satisfaction value and the space satisfaction value were proposed; then the data from the University of California, Berkeley was extracted and the number of vehicles changed lane more often and the vehicle ID were obtained; the BP neural network classification model was established, it was trained and testified by actual data. The results shown the method could predict the intent accurately.

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