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Article
Publication date: 4 January 2013

Gokhan Bayar

The purpose of this paper is to present work which is a part of the Comprehensive Automation for Specialty Crops project (CASC). Desired trajectory tracking objective has…

Abstract

Purpose

The purpose of this paper is to present work which is a part of the Comprehensive Automation for Specialty Crops project (CASC). Desired trajectory tracking objective has been previously performed by using a non‐model based approach in this project. Long distance autonomous drive has been achieved; however the results haven't met the expectations of the project requirements. In order to provide these requirements, this study is conducted. In this study, long distance autonomous trajectory tracking for an orchard vehicle is studied. Besides longitudinal motion, lateral motion of the vehicle is also considered. The longitudinal and lateral errors are objected to keep into a region of less than 10 cm.

Design/methodology/approach

Car‐like robot kinematic modeling approach is used to create desired trajectory. In order to control longitudinal velocity and steering angle of the vehicle, a controller methodology is proposed. Stability of the controller proposed is shown by using Lyapunov stability approach.

Findings

The proposed model is adapted into a four‐wheeled autonomous orchard vehicle and tested in an experimental orchard for long distance autonomous drives. More than 15 km autonomous drive is successfully achieved and the details are presented in this paper.

Originality/value

In this study, long distance autonomous trajectory tracking for an orchard vehicle is focused. A model based control strategy, including the information about longitudinal and lateral motion of the vehicle, is constructed. A new approach to create steering angles for turning operations of the orchard vehicle is introduced. It is objected that the longitudinal and lateral errors should be less than 10 cm during the trajectory tracking task.

Details

Industrial Robot: An International Journal, vol. 40 no. 1
Type: Research Article
ISSN: 0143-991X

Keywords

Abstract

Details

Autonomous Driving
Type: Book
ISBN: 978-1-78714-834-5

Article
Publication date: 4 April 2019

Serkan Ayvaz and Salih Cemil Cetin

The purpose of this paper is to develop a model for autonomous cars to establish trusted parties by combining distributed ledgers and self-driving cars in the traffic to…

Abstract

Purpose

The purpose of this paper is to develop a model for autonomous cars to establish trusted parties by combining distributed ledgers and self-driving cars in the traffic to provide single version of the truth and thus build public trust.

Design/methodology/approach

The model, which the authors call Witness of Things, is based on keeping decision logs of autonomous vehicles in distributed ledgers through the use of vehicular networks and vehicle-to-vehicle/vehicle-to-infrastructure (or vice versa) communications. The model provides a single version of the truth and thus helps enable the autonomous vehicle industry, related organizations and governmental institutions to discover the true causes of road accidents and their consequences in investigations.

Findings

In this paper, the authors explored one of the potential effects of blockchain protocol on autonomous vehicles. The framework provides a solution for operating autonomous cars in an untrusted environment without needing a central authority. The model can also be generalized and applied to other intelligent unmanned systems.

Originality/value

This study proposes a blockchain protocol-based record-keeping model for autonomous cars to establish trusted parties in the traffic and protect single version of the truth.

Details

International Journal of Intelligent Unmanned Systems, vol. 7 no. 2
Type: Research Article
ISSN: 2049-6427

Keywords

Content available
Book part
Publication date: 18 April 2018

Andreas Herrmann, Walter Brenner and Rupert Stadler

Abstract

Details

Autonomous Driving
Type: Book
ISBN: 978-1-78714-834-5

Open Access
Article
Publication date: 12 July 2022

Zheng Xu, Yihai Fang, Nan Zheng and Hai L. Vu

With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.

Abstract

Purpose

With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.

Design/methodology/approach

The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment.

Findings

Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow.

Research limitations/implications

Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.

Practical implications

This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.

Social implications

This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology.

Originality/value

A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.

Details

Journal of Intelligent and Connected Vehicles, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2399-9802

Keywords

Open Access
Article
Publication date: 28 June 2022

Wenhao Yu, Jun Li, Li-Ming Peng, Xiong Xiong, Kai Yang and Hong Wang

The purpose of this paper is to design a unified operational design domain (ODD) monitoring framework for mitigating Safety of the Intended Functionality (SOTIF) risks…

Abstract

Purpose

The purpose of this paper is to design a unified operational design domain (ODD) monitoring framework for mitigating Safety of the Intended Functionality (SOTIF) risks triggered by vehicles exceeding ODD boundaries in complex traffic scenarios.

Design/methodology/approach

A unified model of ODD monitoring is constructed, which consists of three modules: weather condition monitoring for unusual weather conditions, such as rain, snow and fog; vehicle behavior monitoring for abnormal vehicle behavior, such as traffic rule violations; and road condition monitoring for abnormal road conditions, such as road defects, unexpected obstacles and slippery roads. Additionally, the applications of the proposed unified ODD monitoring framework are demonstrated. The practicability and effectiveness of the proposed unified ODD monitoring framework for mitigating SOTIF risk are verified in the applications.

Findings

First, the application of weather condition monitoring demonstrates that the autonomous vehicle can make a safe decision based on the performance degradation of Lidar on rainy days using the proposed monitoring framework. Second, the application of vehicle behavior monitoring demonstrates that the autonomous vehicle can properly adhere to traffic rules using the proposed monitoring framework. Third, the application of road condition monitoring demonstrates that the proposed unified ODD monitoring framework enables the ego vehicle to successfully monitor and avoid road defects.

Originality/value

The value of this paper is that the proposed unified ODD monitoring framework establishes a new foundation for monitoring and mitigating SOTIF risks in complex traffic environments.

Details

Journal of Intelligent and Connected Vehicles, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2399-9802

Keywords

Content available
Book part
Publication date: 18 April 2018

Andreas Herrmann, Walter Brenner and Rupert Stadler

Abstract

Details

Autonomous Driving
Type: Book
ISBN: 978-1-78714-834-5

Article
Publication date: 7 December 2021

K. Balachander, C. Venkatesan and Kumar R.

Autonomous vehicles rely on IoT-based technologies to take numerous decisions in real-time situations. However, added information from the sensor readings will burden the…

Abstract

Purpose

Autonomous vehicles rely on IoT-based technologies to take numerous decisions in real-time situations. However, added information from the sensor readings will burden the system and cause the sensors to produce inaccurate readings. To overcome these issues, this paper aims to focus on communication between sensors and autonomous vehicles for better decision-making in real-time. The system has unique features to detect the upcoming and ongoing vehicles automatically without intervention of humans in the system. It also predicts the type of vehicle and intimates the driver.

Design/methodology/approach

The system is designed using the ATmega 328 P and ESP 8266 chip. Information from ultrasonic and infrared sensors are analyzed and updated in the cloud server. The user can access all these real-time data at any point of time. The stored information in cloud servers is used for integrating artificial intelligence into the system.

Findings

The real-time sensor information is used to predict the surrounding environment and the system responds to the user according to the situation.

Practical implications

The system is implemented on embedded platform with IoT technology. The sensor information is updated to the cloud using the Blynk application for the user in real time.

Originality/value

The system is proposed for smart cities with IoT technology where the user and the system are aware of the surrounding environment. The system is mainly concerned with the accuracy of sensors and the distance between the vehicles in real-time environment.

Details

International Journal of Pervasive Computing and Communications, vol. 17 no. 5
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 19 June 2017

Jiajia Chen, Wuhua Jiang, Pan Zhao and Jinfang Hu

Navigating in off-road environments is a huge challenge for autonomous vehicles, due to the safety requirement, the effects of noises and non-holonomic constraints of…

Abstract

Purpose

Navigating in off-road environments is a huge challenge for autonomous vehicles, due to the safety requirement, the effects of noises and non-holonomic constraints of vehicle. This paper aims to describe a path planning method based on fuzzy support vector machine (FSVM) and general regression neural network (GRNN) that is able to provide a solution path for the autonomous vehicle navigating in the off-road environments.

Design/methodology/approach

The authors decompose the path planning problem into three steps. In the first step, A* algorithm is applied to obtain the positive and negative samples. In the second step, the authors use a learning approach based on radial basis function kernel FSVM to maximize the safety margin for driving, and the fuzzy membership is designed based on GRNN which can help to resolve the problem that the traditional path planning method is easily influenced by noises or outliers. In the third step, the Bezier interpolation algorithm is used to smooth the path. The simulations are designed to verify the parameters of the path planning algorithm.

Findings

The method is implemented on autonomous vehicle and verified against many outdoor scenes. Road test indicates that the proposed method can produce a flexible, smooth and safe path with good anti-jamming performance.

Originality/value

This paper applied a new path planning method based on GRNN-FSVM for autonomous vehicle navigating in off-road environments. GRNN-FSVM can reduce the effects of outliers and maximize the safety margin for driving, the generated path is smooth and safe, while satisfying the constraint of vehicle kinematic.

Details

Industrial Robot: An International Journal, vol. 44 no. 4
Type: Research Article
ISSN: 0143-991X

Keywords

Book part
Publication date: 24 May 2021

Igor Vuletić

This paper is dedicated to the topic of the emerging challenges of traditional criminal law as posed by the development of modern technology. In certain parts of the…

Abstract

This paper is dedicated to the topic of the emerging challenges of traditional criminal law as posed by the development of modern technology. In certain parts of the world, the automotive industry has already implemented a new generation of autonomous self-driving vehicles. Moreover, there have been incidents where such vehicles have been involved in traffic accidents with deadly consequences. The use of autonomous intelligence is also emerging in other important sectors, such as in medicine and the military.

The issue of the legal liability of autonomous machines has been the subject of numerous philosophical debates and approached from the perspective of tort law. The question of criminal liability, however, has still not been debated more comprehensively. In this text, I will analyze the scope and limits of criminal liability of humans for criminal offenses “committed” by autonomous systems. Firstly, I will describe potential crimes of AI in context of intent and negligence. Secondly, I will propose the new concept of (shared) criminal liability, the concept I will name the Division of Labor theory.

Details

The Law and Economics of Patent Damages, Antitrust, and Legal Process
Type: Book
ISBN: 978-1-80071-024-5

Keywords

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