Route planning for agricultural tasks: A general approach for fleets of autonomous vehicles in site-specific herbicide applications

2016 ◽  
Vol 127 ◽  
pp. 204-220 ◽  
Author(s):  
Jesus Conesa-Muñoz ◽  
José María Bengochea-Guevara ◽  
Dionisio Andujar ◽  
Angela Ribeiro
Keyword(s):  
Autonomous Vehicles ◽  
Route Planning ◽  
Site Specific

Route Planning Considerations for Autonomous Vehicles

2018 ◽  
Vol 56 (10) ◽  
pp. 78-84 ◽  
Author(s):  
Abd-Elhamid Taha ◽  
Najah AbuAli
Keyword(s):  
Autonomous Vehicles ◽  
Route Planning

Using ontologies to aid navigation planning in autonomous vehicles

2003 ◽  
Vol 18 (3) ◽  
pp. 243-255 ◽  
Author(s):  
CRAIG SCHLENOFF ◽  
STEPHEN BALAKIRSKY ◽  
MIKE USCHOLD ◽  
RON PROVINE ◽  
SCOTT SMITH
Keyword(s):  
System Architecture ◽  
Automated Reasoning ◽  
Autonomous Vehicles ◽  
Route Planning ◽  
And Performance ◽  
Navigation Planning ◽  
Future Work ◽  
Initial Focus

This paper explores the hypothesis that ontologies can be used to improve the capabilities and performance of on-board route planning for autonomous vehicles. We name a variety of general benefits that ontologies may provide, and list numerous specific ways that ontologies may be used in different components of our chosen infrastructure: the 4D/RCS system architecture developed at NIST. Our initial focus is on simple roadway driving scenarios where the controlled vehicle encounters objects in its path. Our approach is to develop an ontology of objects in the environment, in conjunction with rules for estimating the damage that would be incurred by collisions with the different objects in different situations. Automated reasoning is used to estimate collision damage; this information is fed to the route planner to help it decide whether to avoid the object. We describe our current experiments and plans for future work.

Prediction Capabilities for Cyber Physical Vehicles

2019 ◽  
Vol 1 (1) ◽  
pp. 45-70
Author(s):  
Laszlo Z. Varga
Keyword(s):  
Real Time ◽  
Physical System ◽  
Autonomous Vehicles ◽  
Route Planning ◽  
Traffic Information ◽  
Cyber Physical System ◽  
Time Data ◽  
Real Time Data ◽  
Online Traffic ◽  
State Of The Environment

Cyber physical systems open new ground in the automotive domain. Autonomous vehicles will try to adapt to the changing environment, and decentralized adaptation is a new type of issue that needs to be studied. This article investigates the effects of adaptive route planning when real-time online traffic information is exploited. Simulation results show that if the agents selfishly optimize their actions, then in some situations, the cyber physical system may fluctuate and sometimes the agents may be worse off with real-time data than without real-time data. The proposed solution to this problem is to use anticipatory techniques, where the future state of the environment is predicted from the intentions of the agents. This article concludes with this conjecture: if simultaneous decision-making is prevented, then intention-aware prediction can limit the fluctuation and help the cyber physical system converge to the Nash equilibrium, assuming that the incoming traffic can be predicted.

Research on current method of route planning for autonomous vehicles

2021 ◽  
Author(s):  
Lingshan Luo ◽  
Qingsong Li ◽  
Junhao Yang ◽  
Luping Wang
Keyword(s):  
Autonomous Vehicles ◽  
Route Planning ◽  
Current Method

Ubiquitous IoT in the Automotive Domain

2018 ◽  
pp. 27-51
Author(s):  
Laszlo Z. Varga
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Route Planning ◽  
Traffic Information ◽  
Time Data ◽  
Real Time Data ◽  
New Type ◽  
Simulation Results ◽  
Online Traffic ◽  
State Of The Environment

Ubiquitous IoT systems open new ground in the automotive domain. With the advent of autonomous vehicles, there will be several actors that adapt to changes in traffic, and decentralized adaptation will be a new type of issue that needs to be studied. This chapter investigates the effects of adaptive route planning when real-time online traffic information is exploited. Simulation results show that if the agents selfishly optimize their actions, then in some situations the ubiquitous IoT system may fluctuate and the agents may be worse off with real-time data than without real-time data. The proposed solution to this problem is to use anticipatory techniques, where the future state of the environment is predicted from the intentions of the agents. This chapter concludes with this conjecture: if simultaneous decision making is prevented, then intention-propagation-based prediction can limit the fluctuation and help the ubiquitous IoT system converge to the Nash equilibrium.

scholarly journals A Flexible Multi-Layer Map Model Designed for Lane-Level Route Planning in Autonomous Vehicles

Engineering ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 305-318 ◽  
Author(s):  
Kun Jiang ◽  
Diange Yang ◽  
Chaoran Liu ◽  
Tao Zhang ◽  
Zhongyang Xiao
Keyword(s):  
Autonomous Vehicles ◽  
Route Planning

scholarly journals Application of Artificial Intelligence in Tesla- A Case Study

2021 ◽  
pp. 205-218
Author(s):  
Divya Kumari ◽  
Subrahmanya Bhat
Keyword(s):  
Artificial Intelligence ◽  
Automobile Industry ◽  
Financial Services ◽  
Autonomous Vehicles ◽  
Oil And Gas ◽  
Route Planning ◽  
Case Study Report ◽  
Deep Layers ◽  
Car Park

Background/Purpose: Artificial intelligence algorithms are like humans, performing a task repeatedly, each time changing it slightly to maximize the result. A neural network is made up of several deep layers that allow for learning. Financial services, ICT, life science, oil and gas, retail, automotive, industrial healthcare, and chemicals and manufacturing sectors are among the industries that employ these algorithms. The electric motor is a new concept, and the automobile industry is now undergoing intensive research to determine whether it is practicable and financially viable. There are already some first movers, such as Tesla, who have successfully established their model and are moving forward. Tesla is forcing the auto industry to adapt quickly. Tesla introduced Autopilot driver capability for its Model S vehicle. Tesla Autopilot is a suite of sophisticated driver-assist technologies that include traffic adjustment, congested roads navigation system, autopilot car-parks, computer-controlled road rules, semi-autonomous route planning on major roadways, and the ability to summon the vehicle out of a designated car-park. This article provides a comprehensive analysis of Tesla Company and Innovations of Autopilot Vehicles. Objective: This case study report addresses the growth of Tesla Company in the field of Autonomous Vehicles. Design/Methodology/Approach: The knowledge for this case study of Tesla was gathered from various academic articles, online articles, and the SWOT framework. Findings/Result: Based on the research, this paper discusses the technological histories, Autopilot driving features, safety concerns, financial plans, market challenges, different models, and how Tesla Inc. is accelerating the world's movement in multiple initiatives such as the contribution of the global economic system, study in the Artificial Intelligence and Machine Learning area. Originality/Value: This paper study provides a brief overview of Tesla Inc. given the various data collected, and information about Tesla Autopilot vehicles using Artificial Intelligence based Innovations in Entrepreneurial Oriented Cars. Paper type: A Research Case study paper - focuses on Application of Artificial Intelligence in Tesla Autopilot Vehicles and growth & Journey of the Tesla Inc. Company.

Bridging Cooperative Sensing and Route Planning of Autonomous Vehicles

2012 ◽  
Vol 30 (5) ◽  
pp. 912-922 ◽  
Author(s):  
P. B. Sujit ◽  
Daniel E. Lucani ◽  
Joao B. Sousa
Keyword(s):  
Autonomous Vehicles ◽  
Route Planning ◽  
Cooperative Sensing

scholarly journals Analysis of vehicle stability loss due to strong crosswind gusts using web services in the route planning process

2019 ◽  
Vol 52 (4) ◽  
pp. 47-56
Author(s):  
Igor Betkier ◽  
Szymon Mitkow ◽  
Magdalena Kijek
Keyword(s):  
Autonomous Vehicles ◽  
Intelligent Systems ◽  
Planning Process ◽  
Center Of Mass ◽  
Route Planning ◽  
Weather Forecast ◽  
Weather Conditions ◽  
Stability Loss ◽  
Web Mapping ◽  
Starting Point

Weather conditions play a significant role in road safety. One of its component is a wind influence, which may be affected the moving vehicle from different angles. The final result of such action can take typical kinds of behavior like overturn, slideslip and rotation. Accordingly, vehicles with high side profile are particularly vulnerable to such specific phenomena, what made the planning process more difficult and complex. The article analyses possibilities of a stability loss of a truck vehicle due to strong crosswind gusts. The authors synthesized the model proposed in the literature with available web technologies and the needs of a transport market. Moreover, a method which evaluate a danger of reaching the friction limit by all of the vehicle wheels (slideslip) was developed and is practicable to use. The proposed solution is based on the RESTful API of the weather and web mapping services which allows to collate a direction and a force of wind with a direction of movement and a side profile of the vehicle moving between two locations. What is more a weather forecast is allowed to adopt appropriate variables to compute the air density and evaluate friction coefficients. From the other hand the method uses actual parameters of a vehicle such as axle loads, distance between axles, center of mass location and kind of axle (driven or not). The assumption, which consist in sequencing routes for smaller parts, made it possible to achieve the high accuracy of results on tested areas. The proposed method is simple to implement in any programming language and it can be extended by new functionalities. The analyzed issue can also be a starting point for intelligent systems that can be used in autonomous vehicles.

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