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.

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.

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.

Dynamics of Trust in Automation and Interactive Decision Making during Driving Simulation Tasks

2021 ◽  
Vol 65 (1) ◽  
pp. 786-790
Author(s):  
Lucero Rodriguez Rodriguez ◽  
Carlos Bustamante Orellana ◽  
Jayci Landfair ◽  
Corey Magaldino ◽  
Mustafa Demir ◽  
...  
Keyword(s):  
Decision Making ◽  
Automated System ◽  
Automated Driving ◽  
Safety And Efficacy ◽  
High Performing ◽  
Interactive Decision Making ◽  
Driving Task ◽  
Preliminary Study ◽  
Self Driving Cars ◽  
The Impact

As technological advancements and lowered costs make self-driving cars available to more people, it becomes important to understand the dynamics of human-automation interactions for safety and efficacy. We used a dynamical approach to examine data from a previous study on simulated driving with an automated driving assistant. To maximize effect size in this preliminary study, we focused the current analysis on the two lowest and two highest-performing participants. Our visual comparisons were the utilization of the automated system and the impact of perturbations. Low-performing participants toggled and maintained reliance either on automation or themselves for longer periods of time. Decision making of high-performing participants was using the automation briefly and consistently throughout the driving task. Participants who displayed an early understanding of automation capabilities opted for tactical use. Further exploration of individual differences and automation usage styles will help to understand the optimal human-automation-team dynamic and increase safety and efficacy.

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.

Physiological Indicators of Driver Workload During Car-Following Scenarios and Takeovers in Highly Automated Driving

2021 ◽  
Author(s):  
Vishnu Radhakrishnan ◽  
Natasha Merat ◽  
Tyron Louw ◽  
Rafael Goncalves ◽  
Wei Lyu ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Electrodermal Activity ◽  
Automated Driving ◽  
Physiological Indicators ◽  
Car Following ◽  
Time Headway ◽  
Driver Workload ◽  
Driving Task ◽  
Vehicle Automation ◽  
Lead Vehicle

This driving simulator study, conducted as a part of Horizon2020-funded L3Pilot project, investigated how different car-following situations affected driver workload, within the context of vehicle automation. Electrocardiogram (ECG) and electrodermal activity (EDA)-based physiological metrics were used as objective indicators of workload, along with self-reported workload ratings. A total of 32 drivers were divided into two equal groups, based on whether they engaged in a non-driving related task (NDRT) during automation or monitored the drive. Drivers in both groups were exposed to two counterbalanced experimental drives, lasting ~18 minutes each, of Short (0.5 s) and Long (1.5 s) Time Headway conditions during automated car-following (ACF), which was followed by a takeover that happened with or without a lead vehicle. We observed that the workload on the driver due to the NDRT was significantly higher than both monitoring the drive during ACF and manual car-following (MCF). Furthermore, the results indicated that shorter THWs and the presence of a lead vehicle can significantly increase driver workload during takeover scenarios, potentially affecting the safety of the vehicle. This warrants further research into understanding safe time headway thresholds to be maintained by automated vehicles, without placing additional mental or attentional demands on the driver. To conclude, our results indicated that ECG and EDA signals are sensitive to variations in workload, and hence, warrants further investigation on the value of combining these two signals to assess driver workload in real-time, to help the system respond appropriately to the limitations of the driver and predict their performance in driving task if and when they have to resume manual control of the vehicle.

scholarly journals A Fail-Operational Control Architecture Approach and Dead-Reckoning Strategy in Case of Positioning Failures

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 442
Author(s):  
Jose Angel Matute-Peaspan ◽  
Joshue Perez ◽  
Asier Zubizarreta
Keyword(s):  
Dead Reckoning ◽  
Human Interaction ◽  
Control Architecture ◽  
Minimal Risk ◽  
Operational Control ◽  
Automated Driving ◽  
Risk Condition ◽  
Driving Task ◽  
System Verification ◽  
Set Up

Presently, in the event of a failure in Automated Driving Systems, control architectures rely on hardware redundancies over software solutions to assure reliability or wait for human interaction in takeover requests to achieve a minimal risk condition. As user confidence and final acceptance of this novel technology are strongly related to enabling safe states, automated fall-back strategies must be assured as a response to failures while the system is performing a dynamic driving task. In this work, a fail-operational control architecture approach and dead-reckoning strategy in case of positioning failures are developed and presented. A fail-operational system is capable of detecting failures in the last available positioning source, warning the decision stage to set up a fall-back strategy and planning a new trajectory in real time. The surrounding objects and road borders are considered during the vehicle motion control after failure, to avoid collisions and lane-keeping purposes. A case study based on a realistic urban scenario is simulated for testing and system verification. It shows that the proposed approach always bears in mind both the passenger’s safety and comfort during the fall-back maneuvering execution.

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