scholarly journals Autonomous Driving—A Crash Explained in Detail

2019 ◽  
Vol 9 (23) ◽  
pp. 5126 ◽  
Author(s):  
Betz ◽  
Heilmeier ◽  
Wischnewski ◽  
Stahl ◽  
Lienkamp
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Software Test ◽  
Planning And Control ◽  
Modular Software ◽  
Fatal Crash ◽  
Detailed Explanation ◽  
A Minor ◽  
And Control

Since 2017, a research team from the Technical University of Munich has developed a software stack for autonomous driving. The software was used to participate in the Roborace Season Alpha Championship. The championship aims to achieve autonomous race cars competing with different software stacks against each other. In May 2019, during a software test in Modena, Italy, the greatest danger in autonomous driving became reality: A minor change in environmental influences led an extensively tested software to crash into a barrier at speed. Crashes with autonomous vehicles have happened before but a detailed explanation of why software failed and what part of the software was not working correctly is missing in research articles. In this paper we present a general method that can be used to display an autonomous vehicle disengagement to explain in detail what happened. This method is then used to display and explain the crash from Modena. Firstly a brief introduction into the modular software stack that was used in the Modena event, consisting of three individual parts—perception, planning, and control—is given. Furthermore, the circumstancescausing the crash are elaborated in detail. By presented and explaining in detail which softwarepart failed and contributed to the crash we can discuss further software improvements. As a result, we present necessary functions that need to be integrated in an autonomous driving software stack to prevent such a vehicle behavior causing a fatal crash. In addition we suggest an enhancement of the current disengagement reports for autonomous driving regarding a detailed explanation of the software part that was causing the disengagement. In the outlook of this paper we present two additional software functions for assessing the tire and control performance of the vehicle to enhance the autonomous.

scholarly journals Clothoid: An Integrated Hierarchical Framework for Autonomous Driving in a Dynamic Urban Environment

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5053 ◽  
Author(s):  
Saba Arshad ◽  
Muhammad Sualeh ◽  
Dohyeong Kim ◽  
Dinh Van Nam ◽  
Gon-Woo Kim
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Unified Framework ◽  
Human Errors ◽  
Safe Driving ◽  
Planning And Control ◽  
Starting Point ◽  
And Control

In recent years, research and development of autonomous driving technology have gained much interest. Many autonomous driving frameworks have been developed in the past. However, building a safely operating fully functional autonomous driving framework is still a challenge. Several accidents have been occurred with autonomous vehicles, including Tesla and Volvo XC90, resulting in serious personal injuries and death. One of the major reasons is the increase in urbanization and mobility demands. The autonomous vehicle is expected to increase road safety while reducing road accidents that occur due to human errors. The accurate sensing of the environment and safe driving under various scenarios must be ensured to achieve the highest level of autonomy. This research presents Clothoid, a unified framework for fully autonomous vehicles, that integrates the modules of HD mapping, localization, environmental perception, path planning, and control while considering the safety, comfort, and scalability in the real traffic environment. The proposed framework enables obstacle avoidance, pedestrian safety, object detection, road blockage avoidance, path planning for single-lane and multi-lane routes, and safe driving of vehicles throughout the journey. The performance of each module has been validated in K-City under multiple scenarios where Clothoid has been driven safely from the starting point to the goal point. The vehicle was one of the top five to successfully finish the autonomous vehicle challenge (AVC) in the Hyundai AVC.

scholarly journals V2X-Communication-Aided Autonomous Driving: System Design and Experimental Validation

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2903 ◽  
Author(s):  
Chanyoung Jung ◽  
Daegyu Lee ◽  
Seungwook Lee ◽  
David Hyunchul Shim
Keyword(s):  
Autonomous Vehicle ◽  
Real Life ◽  
Autonomous Driving ◽  
Traffic Information ◽  
Emergency Vehicle ◽  
Driving System ◽  
Planning And Control ◽  
Local Perception ◽  
And Control ◽  
Autonomous Driving System

In recent years, research concerning autonomous driving has gained momentum to enhance road safety and traffic efficiency. Relevant concepts are being applied to the fields of perception, planning, and control of automated vehicles to leverage the advantages offered by the vehicle-to-everything (V2X) communication technology. This paper presents a V2X communication-aided autonomous driving system for vehicles. It is comprised of three subsystems: beyond line-of-sight (BLOS) perception, extended planning, and control. Specifically, the BLOS perception subsystem facilitates unlimited LOS environmental perception through data fusion between local perception using on-board sensors and communication perception via V2X. In the extended planning subsystem, various algorithms are presented regarding the route, velocity, and behavior planning to reflect real-time traffic information obtained utilizing V2X communication. To verify the results, the proposed system was integrated into a full-scale vehicle that participated in the 2019 Hyundai Autonomous Vehicle Competition held in K-city with the V2X infrastructure. Using the proposed system, the authors demonstrated successful completion of all assigned real-life-based missions, including emergency braking caused by a jaywalker, detouring around a construction site ahead, complying with traffic signals, collision avoidance, and yielding the ego-lane for an emergency vehicle. The findings of this study demonstrated the possibility of several potential applications of V2X communication with regard to autonomous driving systems.

Autonomous driving of vehicles based on artificial intelligence

2021 ◽  
pp. 1-10
Author(s):  
Xianping Gao ◽  
Xueliang Bian
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Internet Technology ◽  
General Motors ◽  
Planning And Control ◽  
Fast Development ◽  
Unmanned System ◽  
And Control ◽  
Autonomous Driving System

With the fast development, Internet technology has become a game-changer to the automotive industry. The advances and general applications of high-precision maps make it possible for accurate real-time positioning of vehicles. Meanwhile, the extensive applications of intelligent driving technology make it easier and more intelligent to drive vehicles. This paper reviewed the application of artificial intelligence (AI) in the field of autonomous driving comprehensively and explored the innovative studies of other unmanned motion systems at the same time. Firstly, the hardware architecture of the autonomous driving system is introduced, including five modules as follows: sensing, autonomous driving computer, power supply, signal communication, execution and braking. In addition, General Motors autonomous vehicle is used as an example to introduce its differences from the traditional vehicles in the hardware part. Subsequently, the autonomous driving software is divided into four modules according to functions: positioning, sensing, planning, and control, and the innovative application of artificial intelligence algorithms is introduced. Finally, this paper expands from autonomous driving technology and puts forward an innovative research idea for the intelligent unmanned system.

scholarly journals Curve Path Detection in Autonomous Vehicle using Deep Learning

2018 ◽  
Author(s):  
Balasriram Kodi ◽  
Manimozhi M
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Ground Truth ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Point To Point ◽  
And Control ◽  
Mapping Point

In the field of autonomous vehicles, lane detection and control plays an important role. In autonomous driving the vehicle has to follow the path to avoid the collision. A deep learning technique is used to detect the curved path in autonomous vehicles. In this paper a customized lane detection algorithm was implemented to detect the curvature of the lane. A ground truth labelling tool box for deep learning is used to detect the curved path in autonomous vehicle. By mapping point to point in each frame 80-90% computing efficiency and accuracy is achieved in detecting path.

scholarly journals On the Development of Autonomous Vehicle Safety Distance by an RSS Model Based on a Variable Focus Function Camera

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6733
Author(s):  
Min-Joong Kim ◽  
Sung-Hun Yu ◽  
Tong-Hyun Kim ◽  
Joo-Uk Kim ◽  
Young-Min Kim
Keyword(s):  
Autonomous Vehicles ◽  
Focal Length ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Cruise Control ◽  
Radar Sensors ◽  
Safety And Reliability ◽  
Time Required ◽  
Variable Focus ◽  
Camera Sensors

Today, a lot of research on autonomous driving technology is being conducted, and various vehicles with autonomous driving functions, such as ACC (adaptive cruise control) are being released. The autonomous vehicle recognizes obstacles ahead by the fusion of data from various sensors, such as lidar and radar sensors, including camera sensors. As the number of vehicles equipped with such autonomous driving functions increases, securing safety and reliability is a big issue. Recently, Mobileye proposed the RSS (responsibility-sensitive safety) model, which is a white box mathematical model, to secure the safety of autonomous vehicles and clarify responsibility in the case of an accident. In this paper, a method of applying the RSS model to a variable focus function camera that can cover the recognition range of a lidar sensor and a radar sensor with a single camera sensor is considered. The variables of the RSS model suitable for the variable focus function camera were defined, the variable values were determined, and the safe distances for each velocity were derived by applying the determined variable values. In addition, as a result of considering the time required to obtain the data, and the time required to change the focal length of the camera, it was confirmed that the response time obtained using the derived safe distance was a valid result.

A Biologically Inspired Autonomous Robot Control Based on Behavioural Coordination in Evolutionary Robotics

2011 ◽  
pp. 107-129 ◽  
Author(s):  
José A. Fernández-León ◽  
Gerardo G. Acosta ◽  
Miguel A. Mayosky ◽  
Oscar C. Ibáñez
Keyword(s):  
Mobile Robots ◽  
Autonomous Vehicle ◽  
Trajectory Generation ◽  
Real Life ◽  
Evolutionary Robotics ◽  
Knowledge Based ◽  
Planning And Control ◽  
Life Problems ◽  
Path Planner ◽  
And Control

This work is intended to give an overview of technologies, developed from an artificial intelligence standpoint, devised to face the different planning and control problems involved in trajectory generation for mobile robots. The purpose of this analysis is to give a current context to present the Evolutionary Robotics approach to the problem, which is now being considered as a feasible methodology to develop mobile robots for solving real life problems. This chapter also show the authors’ experiences on related case studies, which are briefly described (a fuzzy logic based path planner for a terrestrial mobile robot, and a knowledge-based system for desired trajectory generation in the Geosub underwater autonomous vehicle). The development of different behaviours within a path generator, built with Evolutionary Robotics concepts, is tested in a Khepera© robot and analyzed in detail. Finally, behaviour coordination based on the artificial immune system metaphor is evaluated for the same application.

scholarly journals Formation Control for a Fleet of Autonomous Ground Vehicles: A Survey

Robotics ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 67 ◽  
Author(s):  
Aakash Soni ◽  
Huosheng Hu
Keyword(s):  
Autonomous Vehicles ◽  
Formation Control ◽  
State Of The Art ◽  
Autonomous Vehicle ◽  
Ground Vehicles ◽  
Implementation Challenges ◽  
Autonomous Ground Vehicles ◽  
Vehicle Platooning ◽  
And Control

Autonomous/unmanned driving is the major state-of-the-art step that has a potential to fundamentally transform the mobility of individuals and goods. At present, most of the developments target standalone autonomous vehicles, which can sense the surroundings and control the vehicle based on this perception, with limited or no driver intervention. This paper focuses on the next step in autonomous vehicle research, which is the collaboration between autonomous vehicles, mainly vehicle formation control or vehicle platooning. To gain a deeper understanding in this area, a large number of the existing published papers have been reviewed systemically. In other words, many distributed and decentralized approaches of vehicle formation control are studied and their implementations are discussed. Finally, both technical and implementation challenges for formation control are summarized.

Development and Testing of an Autonomous Driving Module for Critical Driving Conditions

2008 ◽  
Author(s):  
Francesco Biral ◽  
Enrico Bertolazzi ◽  
Daniele Bortoluzzi ◽  
Paolo Bosetti
Keyword(s):  
Autonomous Vehicles ◽  
Gasoline Engine ◽  
Autonomous Driving ◽  
Great Effort ◽  
Control Laws ◽  
Test Platform ◽  
High Level ◽  
And Control ◽  
Nonlinear Receding Horizon Control ◽  
High Range

In the last years a great effort has been devoted to the development of autonomous vehicles able to drive in a high range of speeds in semi-structured and unstructured environments. This article presents and discusses the software framework for Hardware-In-the-Loop (HIL) and Software-In-the-Loop (SIL) analysis that has been designed for developing and testing of control laws and mission functionalities of semi-autonomous and autonomous vehicles. The ultimate goal of this project is to develop a robotic system, named RUMBy, able to autonomously plan and execute accurate optimal manoeuvres both in normal and in critical driving situations and to be used as a test platform for advanced decision and autonomous driving algorithms. RUMBy’s hardware is a 1:6 scale gasoline engine R/C car with onboard telemetry and control systems. RUMBy’s software consists of three main modules: the manager module that coordinates the other modules and take high level decision; the motion planner module which is based on a Nonlinear Receding Horizon Control (NRHC) algorithm; the actuation module that produces the driving command for the vehicle. The article describes the details of RUMBy architecture and discusses its modular configuration that easily allows HIL and SIL tests.

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