Autonomous Vehicle Navigation With Dynamic Adaptation and Complete Coverage of Unknown Environments

2010 ◽  
Author(s):  
Xin Jin ◽  
Jacqueline M. Luff ◽  
Shalabh Gupta ◽  
Asok Ray
Keyword(s):  
Ising Model ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Dynamic Adaptation ◽  
Complete Coverage ◽  
Navigation Algorithm ◽  
Hazard Detection ◽  
Unknown Environments ◽  
Potential Applications ◽  
Autonomous Vehicle Navigation

This paper presents a Statistical Mechanics-inspired navigation algorithm with dynamic adaptation and complete coverage of unknown environments, which is built upon the concept of generalized Ising model. The algorithm enables autonomous vehicles to cover all areas in the environment, avoid unknown obstacles and adapt to target neighborhoods. Potential applications of this algorithm are humanitarian de-mining, hazard detection and floor-cleaning tasks. The algorithm has been validated on a Player/Stage simulator with an example of minesweeping.

scholarly journals Transporting Children in Autonomous Vehicles: An Exploratory Study

2019 ◽  
Vol 62 (2) ◽  
pp. 278-287 ◽  
Author(s):  
Patrice D. Tremoulet ◽  
Thomas Seacrist ◽  
Chelsea Ward McIntosh ◽  
Helen Loeb ◽  
Anna DiPietro ◽  
...  
Keyword(s):  
Extracurricular Activities ◽  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Vehicle ◽  
Remote Access ◽  
Unaccompanied Children ◽  
Potential Applications ◽  
Minimum Age ◽  
Audio Communication ◽  
Vehicle Information

Objective Identify factors that impact parents’ decisions about allowing an unaccompanied child to ride in an autonomous vehicle (AV). Background AVs are being tested in several U.S. cities and on highways in multiple states. Meanwhile, suburban parents are using ridesharing services to shuttle children from school to extracurricular activities. Parents may soon be able to hire AVs to transport children. Method Nineteen parents of 8- to 16-year-old children, and some of their children, rode in a driving simulator in autonomous mode, then were interviewed. Parents also participated in focus groups. Topics included minimum age for solo child passengers, types of trips unaccompanied children might take, and vehicle features needed to support child passengers. Results Parents would require two-way audio communication and prefer video feeds of vehicle interiors, seatbelt checks, automatic locking, secure passenger identification, and remote access to vehicle information. Parents cited convenience as the greatest benefit and fear that AVs could not protect passengers during unplanned trip interruptions as their greatest concern. Conclusion Manufacturers have an opportunity to design family-friendly AVs from the outset, rather than retrofit them to be safe for child passengers. More research, especially usability studies where families interact with technology prototypes, is needed to understand how AV design impacts child passengers. Application Potential applications of this research include not only designing vehicles that can be used to safely transport children, seniors who no longer drive, and individuals with disabilities but also developing regulations, policies, and societal infrastructure to support safe child transport via AVs.

Grid-based Environment Estimation for Local Autonomous Vehicle Navigation

2016 ◽  
Author(s):  
Georg Tanzmeister
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Local Environment ◽  
Sensor Data ◽  
Test Vehicle ◽  
Safety Critical ◽  
Environment Model ◽  
Reliable Model ◽  
Autonomous Vehicle Navigation ◽  
Grid Based

This dissertation is focused on the environment model for automated vehicles. A reliable model of the local environment available in real-time is a prerequisite to enable almost any useful ­activity performed by a robot, such as planning motions to fulfill tasks. It is particularly important in safety critical applications, such as for autonomous vehicles in regular traffic. In this thesis, novel concepts for local mapping, tracking, the detection of principal moving directions, cost evaluations in motion planning, and road course estimation have been developed. An object- and sensor-independent grid representation forms the basis of all presented methods enabling a generic and robust estimation of the environment. All approaches have been evaluated with sensor data from real road scenarios, and their performance has been experimentally demonstrated with a test vehicle. ...

scholarly journals Sensors and Sensor’s Fusion in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6586
Author(s):  
Andrzej Stateczny ◽  
Marta Wlodarczyk-Sielicka ◽  
Pawel Burdziakowski
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Vehicle Navigation ◽  
Autonomous Vehicle Navigation

Autonomous vehicle navigation has been at the center of several major developments, both in civilian and defense applications [...]

scholarly journals Exploiting Machine Learning for Vision and Motion Planning of Autonomous Vehicles Navigation

2021 ◽  
Vol 19 (3) ◽  
pp. 95-104
Author(s):  
M. Rutendo ◽  
◽  
M. A. Al Akkad ◽  
Keyword(s):  
Machine Learning ◽  
Autonomous Vehicles ◽  
Human Error ◽  
Autonomous Vehicle ◽  
Good Tool ◽  
The Road ◽  
Web Camera ◽  
Gaming Environment ◽  
Error Perception ◽  
Autonomous Vehicle Navigation

The object of this paper is to create a system that can control any vehicle in any gaming environment to simulate, study, experiment and improve how self-driving vehicles operate. It is to be taken as the bases for future work on autonomous vehicles with real hardware devices. The long-term goal is to eliminate human error. Perception, localisation, planning and control subsystems were developed. LiDAR and RADAR sensors were used in addition to a normal web Camera. After getting information from the perception module, the system will be able to localise where the vehicle is, then the planning module is used to plan to which location the vehicle will move, using localisation module data to draw up the best path to use. After knowing the best path, the system will control the vehicle to move autonomously without human help. As a controller a Proportional Integral Derivative PID controller was used. Python programming language, computer vision, and machine learning were used in developing the system, where the only hardware required is a computer with a GPU and powerful graphical card that can run a game which has a vehicle, roads with lane lines and a map of the road. The developed system is intended to be a good tool in conducting experiments for achieving reliable autonomous vehicle navigation.

scholarly journals High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 899 ◽  
Author(s):  
Veli Ilci ◽  
Charles Toth
Keyword(s):  
Autonomous Vehicles ◽  
Moving Objects ◽  
Autonomous Vehicle ◽  
Point Clouds ◽  
Global Navigation Satellite Systems ◽  
Sensor Data ◽  
Measurement Unit ◽  
High Definition ◽  
Mobile Mapping ◽  
Autonomous Vehicle Navigation

Recent developments in sensor technologies such as Global Navigation Satellite Systems (GNSS), Inertial Measurement Unit (IMU), Light Detection and Ranging (LiDAR), radar, and camera have led to emerging state-of-the-art autonomous systems, such as driverless vehicles or UAS (Unmanned Airborne Systems) swarms. These technologies necessitate the use of accurate object space information about the physical environment around the platform. This information can be generally provided by the suitable selection of the sensors, including sensor types and capabilities, the number of sensors, and their spatial arrangement. Since all these sensor technologies have different error sources and characteristics, rigorous sensor modeling is needed to eliminate/mitigate errors to obtain an accurate, reliable, and robust integrated solution. Mobile mapping systems are very similar to autonomous vehicles in terms of being able to reconstruct the environment around the platforms. However, they differ a lot in operations and objectives. Mobile mapping vehicles use professional grade sensors, such as geodetic grade GNSS, tactical grade IMU, mobile LiDAR, and metric cameras, and the solution is created in post-processing. In contrast, autonomous vehicles use simple/inexpensive sensors, require real-time operations, and are primarily interested in identifying and tracking moving objects. In this study, the main objective was to assess the performance potential of autonomous vehicle sensor systems to obtain high-definition maps based on only using Velodyne sensor data for creating accurate point clouds. In other words, no other sensor data were considered in this investigation. The results have confirmed that cm-level accuracy can be achieved.

scholarly journals An Autonomous Vehicle Navigation System Based on Inertial and Visual Sensors

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2952 ◽  
Author(s):  
Xingxing Guang ◽  
Yanbin Gao ◽  
Henry Leung ◽  
Pan Liu ◽  
Guangchun Li
Keyword(s):  
Navigation System ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Visual Navigation ◽  
Measurement Unit ◽  
Random Drift ◽  
Vehicle Navigation System ◽  
Autonomous Vehicle Navigation ◽  
Navigation Accuracy ◽  
Navigation Errors

The strapdown inertial navigation system (SINS) is widely used in autonomous vehicles. However, the random drift error of gyroscope leads to serious accumulated navigation errors during long continuous operation of SINS alone. In this paper, we propose to combine the Inertial Measurement Unit (IMU) data with the line feature parameters from a camera to improve the navigation accuracy. The proposed method can also maintain the autonomy of the navigation system. Experimental results show that the proposed inertial-visual navigation system can mitigate the SINS drift and improve the accuracy, stability, and reliability of the navigation system.

scholarly journals Blockchain Applications to Improve Operation and Security of Transportation Systems: A Survey

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 629
Author(s):  
Navid Khoshavi ◽  
Gabrielle Tristani ◽  
Arman Sargolzaei
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Transportation Systems ◽  
Great Success ◽  
Blockchain Technology ◽  
Fields Of Study ◽  
Vehicle Systems ◽  
Potential Applications ◽  
Near Future ◽  
Broad Overview

Blockchain technology continues to grow and extend into more areas with great success, which highlights the importance of studying the fields that have been, and have yet to be, fundamentally changed by its entrance. In particular, blockchain technology has been shown to be increasingly relevant in the field of transportation systems. More studies continue to be conducted relating to both fields of study and their integration. It is anticipated that their existing relationships will be greatly improved in the near future, as more research is conducted and applications are better understood. Because blockchain technology is still relatively new as compared to older, more well-used methods, many of its future capabilities are still very much unknown. However, before they can be discovered, we need to fully understand past and current developments, as well as expert observations, in applying blockchain technology to the autonomous vehicle field. From an understanding and discussion of the current and potential future capabilities of blockchain technology, as provided through this survey, advancements can be made to create solutions to problems that are inherent in autonomous vehicle systems today. The focus of this paper is mainly on the potential applications of blockchain in the future of transportation systems to be integrated with connected and autonomous vehicles (CAVs) to provide a broad overview on the current related literature and research studies in this field.

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