scholarly journals Updating our understanding of situation awareness in relation to remote operators of autonomous vehicles

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Clare Mutzenich ◽  
Szonya Durant ◽  
Shaun Helman ◽  
Polly Dalton
Keyword(s):  
Situation Awareness ◽  
Autonomous Vehicles ◽  
Automated Driving ◽  
Industry Standard ◽  
Remote Operation ◽  
Regulatory Frameworks ◽  
Human Driver ◽  
Driver Error ◽  
To Come ◽  
Temporary Control

AbstractThe introduction of autonomous vehicles (AVs) could prevent many accidents attributable to human driver error. However, even entirely driverless vehicles will sometimes require remote human intervention. Current taxonomies of automated driving do not acknowledge the possibility of remote control of AVs or the challenges that are unique to such a driver in charge of a vehicle that they are not physically occupying. Yet there are significant differences between situation awareness (SA) in normal driving contexts and SA in these remote driving operations. We argue that the established understanding of automated driving requires updating to include the context of remote operation that is likely to come in to play at higher levels of automation. It is imperative to integrate the role of the remote operator within industry standard taxonomies, so that regulatory frameworks can be established with regards to the training required for remote operation, the necessary equipment and technology, and a comprehensive inventory of the use cases under which we could expect remote operation to be carried out. We emphasise the importance of designing control interfaces in a way that will maximise remote operator (RO) SA and we identify some principles for designing systems aimed at increasing an RO’s sense of embodiment in the AV that requires temporary control.

Autonomous Vehicles’ Car-Following Drivability Evaluation Based on Driving Behavior Spectrum Reference Model

2021 ◽  
pp. 036119812199485
Author(s):  
Xiao Qi ◽  
Ying Ni ◽  
Yiming Xu ◽  
Ye Tian ◽  
Junhua Wang ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Reference Model ◽  
Numerical Test ◽  
Safety Evaluation ◽  
Driving Behavior ◽  
Car Following ◽  
Driving Behaviors ◽  
Human Driver ◽  
Normal Human ◽  
Naturalistic Driving

A large portion of the accidents involving autonomous vehicles (AVs) are not caused by the functionality of AV, but rather because of human intervention, since AVs’ driving behavior was not properly understood by human drivers. Such misunderstanding leads to dangerous situations during interaction between AV and human-driven vehicle (HV). However, few researches considered HV-AV interaction safety in AV safety evaluation processes. One of the solutions is to let AV mimic a normal HV’s driving behavior so as to avoid misunderstanding to the most extent. Therefore, to evaluate the differences of driving behaviors between existing AV and HV is necessary. DRIVABILITY is defined in this study to characterize the similarity between AV’s driving behaviors and expected behaviors by human drivers. A driving behavior spectrum reference model built based on human drivers’ behaviors is proposed to evaluate AVs’ car-following drivability. The indicator of the desired reaction time (DRT) is proposed to characterize the car-following drivability. Relative entropy between the DRT distribution of AV and that of the entire human driver population are used to quantify the differences between driving behaviors. A human driver behavior spectrum was configured based on naturalistic driving data by human drivers collected in Shanghai, China. It is observed in the numerical test that amongst all three types of preset AVs in the well-received simulation package VTD, the brisk AV emulates a normal human driver to the most extent (ranking at 55th percentile), while the default AV and the comfortable AV rank at 35th and 8th percentile, respectively.

scholarly journals Effects of User Interfaces on Take-Over Performance: A Review of the Empirical Evidence

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 162
Author(s):  
Soyeon Kim ◽  
René van Egmond ◽  
Riender Happee
Keyword(s):  
User Interface ◽  
Situation Awareness ◽  
User Interfaces ◽  
Situational Awareness ◽  
Selection Criterion ◽  
Auditory Modality ◽  
Automated Driving ◽  
Automated Vehicle ◽  
Positive Effects ◽  
High Urgency

In automated driving, the user interface plays an essential role in guiding transitions between automated and manual driving. This literature review identified 25 studies that explicitly studied the effectiveness of user interfaces in automated driving. Our main selection criterion was how the user interface (UI) affected take-over performance in higher automation levels allowing drivers to take their eyes off the road (SAE3 and SAE4). We categorized user interface (UI) factors from an automated vehicle-related information perspective. Short take-over times are consistently associated with take-over requests (TORs) initiated by the auditory modality with high urgency levels. On the other hand, take-over requests directly displayed on non-driving-related task devices and augmented reality do not affect take-over time. Additional explanations of take-over situation, surrounding and vehicle information while driving, and take-over guiding information were found to improve situational awareness. Hence, we conclude that advanced user interfaces can enhance the safety and acceptance of automated driving. Most studies showed positive effects of advanced UI, but a number of studies showed no significant benefits, and a few studies showed negative effects of advanced UI, which may be associated with information overload. The occurrence of positive and negative results of similar UI concepts in different studies highlights the need for systematic UI testing across driving conditions and driver characteristics. Our findings propose future UI studies of automated vehicle focusing on trust calibration and enhancing situation awareness in various scenarios.

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.

Challenging human driver taxis with shared autonomous vehicles: a case study of Chicago

2019 ◽  
Vol 12 (10) ◽  
pp. 701-705 ◽  
Author(s):  
Jun Liu ◽  
Steven Jones ◽  
Emmanuel Kofi Adanu
Keyword(s):  
Autonomous Vehicles ◽  
Human Driver

scholarly journals Imitating a Safe Human Driver Behaviour in Roundabouts Through Deep Learning

2020 ◽  
Vol 19 (1) ◽  
pp. 85-88
Author(s):  
A. S. J. Cervera ◽  
F. J. Alonso ◽  
F. S. García ◽  
A. D. Alvarez
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Driver Behaviour ◽  
Safety Issues ◽  
Current State ◽  
Surrounding Environment ◽  
Human Driver ◽  
State Of The Environment

Roundabouts provide safe and fast circulation as well as many environmental advantages, but drivers adopting unsafe behaviours while circulating through them may cause safety issues, provoking accidents. In this paper we propose a way of training an autonomous vehicle in order to behave in a human and safe way when entering a roundabout. By placing a number of cameras in our vehicle and processing their video feeds through a series of algorithms, including Machine Learning, we can build a representation of the state of the surrounding environment. Then, we use another set of Deep Learning algorithms to analyze the data and determine the safest way of circulating through a roundabout given the current state of the environment, including nearby vehicles with their estimated positions, speeds and accelerations. By watching multiple attempts of a human entering a roundabout with both safe and unsafe behaviours, our second set of algorithms can learn to mimic the human’s good attempts and act in the same way as him, which is key to a safe implementation of autonomous vehicles. This work details the series of steps that we took, from building the representation of our environment to acting according to it in order to attain safe entry into single lane roundabouts.

scholarly journals The Oil and Gas ROFR: Understanding Current ROFR Issues from the Point of View of the Transactional Lawyer, the Litigator, and In-House Counsel

2017 ◽  
Author(s):  
Donald G. MacDiarmid ◽  
Sean J. Korney ◽  
Melanie Teetaert ◽  
Julie J.M. Taylor ◽  
Robert Martz ◽  
...  
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Point Of View ◽  
Economic Consequences ◽  
Complex Nature ◽  
Gas Industry ◽  
Industry Standard ◽  
The Law ◽  
To Come ◽  
Corporate Lawyers

Rights of first refusal and other preferential or pre-emptive rights (together, ROFRs, and individually a ROFR) routinely find their way into oil and gas industry agreements. Disputes often arise because of the complex nature and significant economic consequences of ROFRs. In recent years, a number of reported cases, either relating directly to ROFRs or more generally relating to contractual interpretation, have clarified (or at times muddied) the waters surrounding the use, application, and interpretation of ROFRs. However, most ROFR disputes never result in a reported decision because the parties typically negotiate solutions long before trial.The authors consider current trends involving ROFRs in oil and gas agreements, and how they believe the law and legal practice surrounding ROFRs might continue to evolve in the years to come. The authors do not attempt to rehash the fundamentals of the law surrounding ROFRs; instead, they focus on how the courts have dealt with ROFRs in recent cases as well as how corporate lawyers and in-house counsel grapple with ROFRs day-today. The authors utilize the ROFR provisions found in industry standard contracts to analyze outstanding areas of uncertainty as well as what lawyers should contemplate prior to including a ROFR in an agreement. Additionally, the article examines the implications of recent rulings on the duty of good faith that may affect ROFRs. Finally, the article considers selected subjects of topical interest, including ROFRs in the context of busted butterfly transactions, insolvency proceedings, and package deals.

scholarly journals The Identification of Non-Driving Activities with Associated Implication on the Take-Over Process

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 42
Author(s):  
Lichao Yang ◽  
Mahdi Babayi Semiromi ◽  
Yang Xing ◽  
Chen Lv ◽  
James Brighton ◽  
...  
Keyword(s):  
Situation Awareness ◽  
Haptic Feedback ◽  
Hand Movement ◽  
Transition Process ◽  
Recognition System ◽  
Steering Wheel ◽  
Automated Driving ◽  
Driver Behaviour ◽  
Active Mode ◽  
The Impact

In conditionally automated driving, the engagement of non-driving activities (NDAs) can be regarded as the main factor that affects the driver’s take-over performance, the investigation of which is of great importance to the design of an intelligent human–machine interface for a safe and smooth control transition. This paper introduces a 3D convolutional neural network-based system to recognize six types of driver behaviour (four types of NDAs and two types of driving activities) through two video feeds based on head and hand movement. Based on the interaction of driver and object, the selected NDAs are divided into active mode and passive mode. The proposed recognition system achieves 85.87% accuracy for the classification of six activities. The impact of NDAs on the perspective of the driver’s situation awareness and take-over quality in terms of both activity type and interaction mode is further investigated. The results show that at a similar level of achieved maximum lateral error, the engagement of NDAs demands more time for drivers to accomplish the control transition, especially for the active mode NDAs engagement, which is more mentally demanding and reduces drivers’ sensitiveness to the driving situation change. Moreover, the haptic feedback torque from the steering wheel could help to reduce the time of the transition process, which can be regarded as a productive assistance system for the take-over process.

scholarly journals Taxonomy of Object Detection methods: A Survey

2022 ◽  
Vol 10 (1) ◽  
pp. 20-25
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Real Life ◽  
Object Motion ◽  
Detection Methods ◽  
Illumination Variation ◽  
Human Machine Interaction ◽  
Research Areas ◽  
To Come ◽  
Point Detection

Object detection (OD) within a video is one of the relevant and critical research areas in the computer vision field. Due to the widespread of Artificial Intelligence, the basic principle in real life nowadays and its exponential growth predicted in the epochs to come, it will transmute the public. Object Detection has been extensively implemented in several areas, including human-machine Interaction, autonomous vehicles, security with video surveillance, and various fields that will be mentioned further. However, this augmentation of OD tackles different challenges such as occlusion, illumination variation, object motion, without ignoring the real-time aspect that can be quite problematic. This paper also includes some methods of application to take into account these issues. These techniques are divided into five subcategories: Point Detection, segmentation, supervised classifier, optical flow, a background modeling. This survey decorticates various methods and techniques used in object detection, as well as application domains and the problems faced. Our study discusses the cruciality of deep learning algorithms and their efficiency on future improvement in object detection topics within video sequences.

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