Research on current method of route planning for autonomous vehicles

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.

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Prediction Capabilities for Cyber Physical Vehicles

International Journal of Cyber-Physical Systems ◽

10.4018/ijcps.2019010104 ◽

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.

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Ubiquitous IoT in the Automotive Domain

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Solutions for Cyber-Physical Systems Ubiquity ◽

10.4018/978-1-5225-2845-6.ch002 ◽

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.

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

Engineering ◽

10.1016/j.eng.2018.11.032 ◽

2019 ◽

Vol 5 (2) ◽

pp. 305-318 ◽

Cited By ~ 5

Author(s):

Kun Jiang ◽

Diange Yang ◽

Chaoran Liu ◽

Tao Zhang ◽

Zhongyang Xiao

Keyword(s):

Autonomous Vehicles ◽

Route Planning

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Application of Artificial Intelligence in Tesla- A Case Study

International Journal of Applied Engineering and Management Letters ◽

10.47992/ijaeml.2581.7000.0113 ◽

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.

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Bridging Cooperative Sensing and Route Planning of Autonomous Vehicles

IEEE Journal on Selected Areas in Communications ◽

10.1109/jsac.2012.120607 ◽

2012 ◽

Vol 30 (5) ◽

pp. 912-922 ◽

Cited By ~ 18

Author(s):

P. B. Sujit ◽

Daniel E. Lucani ◽

Joao B. Sousa

Keyword(s):

Autonomous Vehicles ◽

Route Planning ◽

Cooperative Sensing

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An Intelligent Actuator of an Indoor Logistics System Based on Multi-Sensor Fusion

Actuators ◽

10.3390/act10060120 ◽

2021 ◽

Vol 10 (6) ◽

pp. 120

Author(s):

Pangwei Wang ◽

Yunfeng Wang ◽

Xu Wang ◽

Ying Liu ◽

Juan Zhang

Keyword(s):

Intelligent Transportation Systems ◽

Autonomous Vehicles ◽

Inertial Navigation System ◽

Route Planning ◽

Autonomous Driving ◽

Transportation Systems ◽

A Algorithm ◽

Logistics System ◽

Planning Strategy ◽

Logistics Distribution

Integration technologies of artificial intelligence (AI) and autonomous vehicles play important roles in intelligent transportation systems (ITS). In order to achieve better logistics distribution efficiency, this paper proposes an intelligent actuator of an indoor logistics system by fusing multiple involved sensors. Firstly, an actuator based on a four-wheel differential chassis is equipped with sensors, including an RGB camera, a lidar and an indoor inertial navigation system, by which autonomous driving can be realized. Secondly, cross-floor positioning can be realized by multi-node simultaneous localization and mappings (SLAM) based on the Cartographer algorithm Thirdly the actuator can communicate with elevators and take the elevator to the designated delivery floor. Finally, a novel indoor route planning strategy is designed based on an A* algorithm and genetic algorithm (GA) and an actual building is tested as a scenario. The experimental results have shown that the actuator can model the indoor mapping and develop the optimal route effectively. At the same time, the actuator displays its superiority in detecting the dynamic obstacles and actively avoiding the collision in the indoor scenario. Through communicating with indoor elevators, the final delivery task can be completed accurately by autonomous driving.

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