scholarly journals Warning Apps for Road Safety: A Technological and Economical Perspective for Autonomous Driving – The Warning Task in the Transition from Human Driver to Automated Driving

2021 ◽  
Vol 37 (4) ◽  
pp. 363-377
Author(s):  
Johanna Trager ◽  
Lenka Kalová ◽  
Raphaela Pagany ◽  
Wolfgang Dorner
Keyword(s):  
Road Safety ◽  
Autonomous Driving ◽  
Automated Driving ◽  
Human Driver

scholarly journals A Few Critical Human Factors for Developing Sustainable Autonomous Driving Technology

2020 ◽  
Vol 12 (7) ◽  
pp. 3030
Author(s):  
José Fernando Sabando Cárdenas ◽  
Jong Gyu Shin ◽  
Sang Ho Kim
Keyword(s):  
Human Factors ◽  
User Satisfaction ◽  
Autonomous Driving ◽  
Software Components ◽  
Automated Driving ◽  
Human Errors ◽  
Learning Speed ◽  
Human Driver ◽  
Level 3 ◽  
Self Driving Cars

The purpose of this study is to develop a framework that can identify critical human factors (HFs) that can generate human errors and, consequently, accidents in autonomous driving level 3 situations. Although much emphasis has been placed on developing hardware and software components for self-driving cars, interactions between a human driver and an autonomous car have not been examined. Because user acceptance and trust are substantial for the further and sustainable development of autonomous driving technology, considering factors that will influence user satisfaction is crucial. As autonomous driving is a new field of research, the literature review in other established fields was performed to draw out these probable HFs. Herein, interrelationship matrices were deployed to identify critical HFs and analyze the associations between these HFs and their impact on performance. Age, focus, multitasking capabilities, intelligence, and learning speed are selected as the most critical HFs in autonomous driving technology. Considering these factors in designing interactions between drivers and automated driving systems will enhance users’ acceptance of the technology and its sustainability by securing good usability and user experiences.

scholarly journals Autonomous Driving – How to Apply Safety Principles

Dependability ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 21-33
Author(s):  
H. Schӓbe
Keyword(s):  
Autonomous Driving ◽  
Automated Driving ◽  
Safety Standards ◽  
The Road ◽  
Safe Life ◽  
Level Of Automation ◽  
Human Driver ◽  
On The Road ◽  
Fail Safe ◽  
Automatic Systems

We discuss safety principles of autonomous driving road vehicles. First, we provide a comparison between principles and experience of autonomous or automatic systems on rails and on the road. An automatic metro operates in a controlled and well-defined environment, passengers and third persons are separated from driving trains by fences, tunnels, etc. A road vehicle operates in a much more complex environment. Further, we discuss safety principles. The application of safety principles (e.g. fail-safe or safe-life) is used to design and implement a safe system that eventually fulfils the requirements of the functional safety standards. The different responsibility of human driver and technical driving system in different automation levels for autonomous driving vehicles require the application of safety principles. We consider, which safety principles have to be applied using general safety principles and analysing the relevant SAE level based on the experience from projects for the five levels of automated driving as defined by the SAE. Depending on the level of automation, the technical systems are implemented as fail-silent, fails-safe or as safe-life.

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.

Independent wheel control system design for highly automated driving

2021 ◽  
pp. 095440702110065
Author(s):  
Shihuan Li ◽  
Lei Wang
Keyword(s):  
Control System ◽  
Control Method ◽  
Autonomous Driving ◽  
Control System Design ◽  
Steering Control ◽  
Automated Driving ◽  
Accuracy And Precision ◽  
Actual Observation ◽  
Control Scheme ◽  
Interference Measurement

For L4 and above autonomous driving levels, the automatic control system has been redundantly designed, and a new steering control method based on brake has been proposed; a new dual-track model has been established through multiple driving tests. The axle part of the model was improved, the accuracy of the transfer function of the model was verified again through acceleration-slide tests; a controller based on interference measurement was designed on the basis of the model, and the relationships between the controller parameters was discussed. Through the linearization of the controller, the robustness of uncertain automobile parameters is discussed; the control scheme is tested and verified through group driving test, and the results prove that the accuracy and precision of the controller meet the requirements, the robustness stability is good. Moreover, the predicted value of the model fits well with the actual observation value, the proposal of this method provides a new idea for avoiding car out of control.

Decision making at unsignalized inner city intersections using discrete events systems

2022 ◽  
Vol 0 (0) ◽  
Author(s):  
Hannes Weinreuter ◽  
Balázs Szigeti ◽  
Nadine-Rebecca Strelau ◽  
Barbara Deml ◽  
Michael Heizmann
Keyword(s):  
Inner City ◽  
Road Safety ◽  
Autonomous Vehicles ◽  
Discrete Event ◽  
Autonomous Driving ◽  
Simulation Environment ◽  
Discrete Events ◽  
Real World Data ◽  
Event System ◽  
Discrete Events Systems

Abstract Autonomous driving is a promising technology to, among many aspects, improve road safety. There are however several scenarios that are challenging for autonomous vehicles. One of these are unsignalized junctions. There exist scenarios in which there is no clear regulation as to is allowed to drive first. Instead, communication and cooperation are necessary to solve such scenarios. This is especially challenging when interacting with human drivers. In this work we focus on unsignalized T-intersections. For that scenario we propose a discrete event system (DES) that is able to solve the cooperation with human drivers at a T-intersection with limited visibility and no direct communication. The algorithm is validated in a simulation environment, and the parameters for the algorithm are based on an analysis of typical human behavior at intersections using real-world data.

scholarly journals Development of Deep Learning Based Human-Centered Threat Assessment for Application to Automated Driving Vehicle

2019 ◽  
Vol 10 (1) ◽  
pp. 253 ◽  
Author(s):  
Donghoon Shin ◽  
Hyun-geun Kim ◽  
Kang-moon Park ◽  
Kyongsu Yi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Threat Assessment ◽  
Collision Probability ◽  
Driver Model ◽  
Sequential Data ◽  
Automated Driving ◽  
Human Driver ◽  
Acceleration And Deceleration ◽  
The Individual

This paper describes the development of deep learning based human-centered threat assessment for application to automated driving vehicle. To achieve naturalistic driver model that would feel natural while safe to a human driver, manual driving characteristics are investigated through real-world driving test data. A probabilistic threat assessment with predicted collision time and collision probability is conducted to evaluate driving situations. On the basis of collision risk analysis, two kinds of deep learning have been implemented to reflect human driving characteristics for automated driving. A deep neural network (DNN) and recurrent neural network (RNN) are designed by neural architecture search (NAS), and by learning from the sequential data, respectively. The NAS is used to automatically design the individual driver’s neural network for efficient and effortless design process while ensuring training performance. Sequential trends in the host vehicle’s state can be incorporated through hand-made RNN. It has been shown from human-centered risk assessment simulations that two successfully designed deep learning driver models can provide conservative and progressive driving behavior similar to a manual human driver in both acceleration and deceleration situations by preventing collision.

Do Autonomous Vehicle Driving Styles Affect User State?: A Preliminary Investigation

2019 ◽  
Author(s):  
Yuan Shi ◽  
Wenhui Huang ◽  
Federico Cheli ◽  
Monica Bordegoni ◽  
Giandomenico Caruso
Keyword(s):  
Driving Simulator ◽  
Autonomous Vehicle ◽  
Preliminary Investigation ◽  
Autonomous Systems ◽  
Autonomous Driving ◽  
Point Of View ◽  
Automated Driving ◽  
Perspective View ◽  
Self Assessment ◽  
Driving Experience

Abstract A bursting number of achievements in the autonomous vehicle industry have been obtained during the past decades. Various systems have been developed to make automated driving possible. Due to the algorithm used in the autonomous vehicle system, the performance of the vehicle differs from one to another. However, very few studies have given insight into the influence caused by implementing different algorithms from a human factors point of view. Two systems based on two algorithms with different characteristics are utilized to generate the two driving styles of the autonomous vehicle, which are implemented into a driving simulator in order to create the autonomous driving experience. User’s skin conductance (SC) data, which enables the evaluation of user’s cognitive workload and mental stress were recorded and analyzed. Subjective measures were applied by filling out Swedish occupational fatigue inventory (SOFI-20) to get a user self-reporting perspective view of their behavior changes along with the experiments. The results showed that human’s states were affected by the driving styles of different autonomous systems, especially in the period of speed variation. By analyzing users’ self-assessment data, a correlation was observed between the user “Sleepiness” and the driving style of the autonomous vehicle. These results would be meaningful for the future development of the autonomous vehicle systems, in terms of balancing the performance of the vehicle and user’s experience.

Effects of Task-Induced Fatigue in Prolonged Conditional Automated Driving

2019 ◽  
Vol 61 (7) ◽  
pp. 1186-1199 ◽  
Author(s):  
Oliver Jarosch ◽  
Hanna Bellem ◽  
Klaus Bengler
Keyword(s):  
Automated Driving ◽  
Good Opportunity ◽  
Monitoring Task ◽  
Human Driver

Objective: The aim of this study was to investigate the effects of task-induced fatigue in prolonged conditional automated driving on takeover performance. Background: In conditional automated driving, the driver can engage in non–driving related tasks (NDRTs) and does not have to monitor the system and the driving environment. In the event that the system hits its limits, the human driver must regain control of the car. To ensure safety, adequate driver fallback performance is necessary. Effects of the drivers’ state and the engagement in NDRTs need to be investigated. Method: Seventy-three participants experienced prolonged conditional automated rides and simultaneously had to engage in either an activating quiz or a fatiguing monitoring task (between subjects). After 50 minutes, a takeover situation occurred, and participants had to regain control of the car. Results: Prolonged conditional automated driving and simultaneously engaging in NDRTs affected the driver’s state and the takeover performance of the participants. Takeover performance was impaired when participants had to deal with monotonous NDRTs. Conclusion: An engagement in monotonous monitoring tasks in conditional automated driving affects the driver’s state and takeover performance when it comes to takeover situations. Especially in prolonged automated driving, an adequate driver state seems to be necessary for safety reasons. Application: The results of this study demonstrate that engagement in monotonous NDRTs while driving conditionally automated may negatively affect takeover performance. A monitoring of the driver state and adapted assistance in a takeover situation seems to be a good opportunity to ensure safety.

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