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.

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 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 Randomized Adversarial Imitation Learning for Autonomous Driving

2019 ◽  
Author(s):  
MyungJae Shin ◽  
Joongheon Kim
Keyword(s):  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Imitation Learning ◽  
Cruise Control ◽  
Driving System ◽  
Derivative Free Optimization ◽  
Derivative Free ◽  
Multi Agent ◽  
Lane Keeping ◽  
Autonomous Driving System

With the evolution of various advanced driver assistance system (ADAS) platforms, the design of autonomous driving system is becoming more complex and safety-critical. The autonomous driving system simultaneously activates multiple ADAS functions; and thus it is essential to coordinate various ADAS functions. This paper proposes a randomized adversarial imitation learning (RAIL) method that imitates the coordination of autonomous vehicle equipped with advanced sensors. The RAIL policies are trained through derivative-free optimization for the decision maker that coordinates the proper ADAS functions, e.g., smart cruise control and lane keeping system. Especially, the proposed method is also able to deal with the LIDAR data and makes decisions in complex multi-lane highways and multi-agent environments.

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.

Autonomy and Responsibility in Hybrid Systems

2017 ◽  
Author(s):  
Wulf Loh ◽  
Janina Loh
Keyword(s):  
Hybrid Systems ◽  
Hybrid System ◽  
Moral Dilemma ◽  
Autonomous Driving ◽  
Shared Responsibility ◽  
Driving System ◽  
Autonomous Cars ◽  
Self Driving Cars ◽  
Trolley Cases ◽  
Autonomous Driving System

In this chapter, we give a brief overview of the traditional notion of responsibility and introduce a concept of distributed responsibility within a responsibility network of engineers, driver, and autonomous driving system. In order to evaluate this concept, we explore the notion of man–machine hybrid systems with regard to self-driving cars and conclude that the unit comprising the car and the operator/driver consists of such a hybrid system that can assume a shared responsibility different from the responsibility of other actors in the responsibility network. Discussing certain moral dilemma situations that are structured much like trolley cases, we deduce that as long as there is something like a driver in autonomous cars as part of the hybrid system, she will have to bear the responsibility for making the morally relevant decisions that are not covered by traffic rules.

Oops! It's Too Late. Your Autonomous Driving System Needs a Faster Middleware

2021 ◽  
Vol 6 (4) ◽  
pp. 7301-7308
Author(s):  
Tianze Wu ◽  
Baofu Wu ◽  
Sa Wang ◽  
Liangkai Liu ◽  
Shaoshan Liu ◽  
...  
Keyword(s):  
Autonomous Driving ◽  
Driving System ◽  
Autonomous Driving System

An Autonomous Driving System for Unknown Environments Using a Unified Map

2015 ◽  
Vol 16 (4) ◽  
pp. 1999-2013 ◽  
Author(s):  
Inwook Shim ◽  
Jongwon Choi ◽  
Seunghak Shin ◽  
Tae-Hyun Oh ◽  
Unghui Lee ◽  
...  
Keyword(s):  
Autonomous Driving ◽  
Driving System ◽  
Unknown Environments ◽  
Autonomous Driving System

UGV autonomous driving system design for unstructed environment

2021 ◽  
Author(s):  
Jingqin Zhang ◽  
Jun Hou ◽  
Jinwen Hu ◽  
Chunhui Zhao ◽  
Zhao Xu ◽  
...  
Keyword(s):  
System Design ◽  
Autonomous Driving ◽  
Driving System ◽  
Autonomous Driving System
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