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You searched for subject:(Infrastructure enabled autonomy). Showing records 1 – 3 of 3 total matches.

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Texas A&M University

1. Nayak, Abhishek. Development of Vision-based Response of Autonomous Vehicles Towards Emergency Vehicles Using Infrastructure Enabled Autonomy.

Degree: MS, Mechanical Engineering, 2019, Texas A&M University

The effectiveness of law enforcement and public safety is directly dependent on the time taken by first responders to arrive at the scene of an emergency. The primary objective of this thesis is to develop techniques and actions of response for an autonomous vehicle in emergency scenarios. This work discusses the methods developed to identify Emergency Vehicles (EV) and use its localized information to develop response actions for autonomous vehicles in emergency scenarios using an Infrastructure-Enabled Autonomy (IEA) setup. IEA is a new paradigm in autonomous vehicles research that aims at distributed intelligence architecture by transferring the core functionalities of sensing and localization to a roadside infrastructure setup. In this work two independent frameworks were developed to identify Emergency vehicles in a video feed using computer vision techniques: (1) A one-stage framework where an object detection algorithm is trained on a custom dataset to detect EVs, (2) A two-stage framework where an object classification is independently implemented in series with an object detection pipeline to classify vehicles into EVs and nonEVs. The performance of many popular classification models were compared on a combination of multi-spectral feature vectors of an image to identify the ideal combination to be used for EV identification rule. Localized position co-ordinates of an EV are obtained by deploying the classification routine on IEA. This position information is used as an input in an autonomous vehicle and an ideal response action is developed. Advisors/Committee Members: Rathinam, Sivakumar (advisor), Gopalswamy, Swaminathan (committee member), Chrysler, Susan (committee member).

Subjects/Keywords: Object detection; Tracking; Control; Infrastructure enabled Autonomy; Autonomous Vehicles

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APA (6th Edition):

Nayak, A. (2019). Development of Vision-based Response of Autonomous Vehicles Towards Emergency Vehicles Using Infrastructure Enabled Autonomy. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/188782

Chicago Manual of Style (16th Edition):

Nayak, Abhishek. “Development of Vision-based Response of Autonomous Vehicles Towards Emergency Vehicles Using Infrastructure Enabled Autonomy.” 2019. Masters Thesis, Texas A&M University. Accessed April 12, 2021. http://hdl.handle.net/1969.1/188782.

MLA Handbook (7th Edition):

Nayak, Abhishek. “Development of Vision-based Response of Autonomous Vehicles Towards Emergency Vehicles Using Infrastructure Enabled Autonomy.” 2019. Web. 12 Apr 2021.

Vancouver:

Nayak A. Development of Vision-based Response of Autonomous Vehicles Towards Emergency Vehicles Using Infrastructure Enabled Autonomy. [Internet] [Masters thesis]. Texas A&M University; 2019. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/1969.1/188782.

Council of Science Editors:

Nayak A. Development of Vision-based Response of Autonomous Vehicles Towards Emergency Vehicles Using Infrastructure Enabled Autonomy. [Masters Thesis]. Texas A&M University; 2019. Available from: http://hdl.handle.net/1969.1/188782


Texas A&M University

2. Burch, Austin Jess. Infrastructure Enabled Autonomy Acting as an Intelligent Transportation System for Autonomous Cars.

Degree: MS, Mechanical Engineering, 2018, Texas A&M University

Autonomous cars have the ability to increase safety, efficiency, and speed of travel. Yet many see a point at which stand-alone autonomous agents populate an area too densely, creating increased risk - particularly when each agent is operating and making decisions on its own and in its own self-interest. The problem at hand then becomes how to best implement and scale this new technology and structure in such a way that it can keep pace with a rapidly changing world, benefitting not just individuals, but societies. This research approaches the challenge by developing an intelligent transportation system that relies on an infrastructure. The solution lies in the removal of sensing and high computational tasks from the vehicles, allowing static ground stations with multi sensor-sensing packs to sense the surrounding environment and direct the vehicles safely from start to goal. On a high level, the Infrastructure Enabled Autonomy system (IEA) uses less hardware, bandwidth, energy, and money to maintain a controlled environment for a vehicle to operate when in highly congested environments. Through the development of background detection algorithms, this research has shown the advantage of static MSSPs analyzing the same environment over time, and carrying an increased reliability from fewer unknowns about the area of interest. It was determined through testing that wireless commands can sufficiently operate a vehicle in a limited agent environment, and do not bottleneck the system. The horizontal trial outcome illustrated that a switching MSSP state of the IEA system showed similar loop time, but a greatly increased standard deviation. However, after performing a t-test with a 95 percent confidence interval, the static and switching MSSP state trials were not significantly different. The final testing quantified the cross track error. For a straight path, the vehicle being controlled by the IEA system had a cross track error less than 12 centimeters, meaning between the controller, network lag, and pixel error, the system was robust enough to generate stable control of the vehicle with minimal error. Advisors/Committee Members: Saripalli, Srikanth (advisor), Gopalswamy, Swaminathan (committee member), Carlson, Paul (committee member).

Subjects/Keywords: Infrastructure Enabled Autonomy; Intelligent Transportation System; Autonomous Cars; Distributed Computing; Remote Sensing; Autonomous Vehicle; Unmanned System

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Burch, A. J. (2018). Infrastructure Enabled Autonomy Acting as an Intelligent Transportation System for Autonomous Cars. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/173594

Chicago Manual of Style (16th Edition):

Burch, Austin Jess. “Infrastructure Enabled Autonomy Acting as an Intelligent Transportation System for Autonomous Cars.” 2018. Masters Thesis, Texas A&M University. Accessed April 12, 2021. http://hdl.handle.net/1969.1/173594.

MLA Handbook (7th Edition):

Burch, Austin Jess. “Infrastructure Enabled Autonomy Acting as an Intelligent Transportation System for Autonomous Cars.” 2018. Web. 12 Apr 2021.

Vancouver:

Burch AJ. Infrastructure Enabled Autonomy Acting as an Intelligent Transportation System for Autonomous Cars. [Internet] [Masters thesis]. Texas A&M University; 2018. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/1969.1/173594.

Council of Science Editors:

Burch AJ. Infrastructure Enabled Autonomy Acting as an Intelligent Transportation System for Autonomous Cars. [Masters Thesis]. Texas A&M University; 2018. Available from: http://hdl.handle.net/1969.1/173594


Utah State University

3. Liu, Zhaocai. Strategic Infrastructure Planning for Autonomous Vehicles.

Degree: PhD, Civil and Environmental Engineering, 2020, Utah State University

Compared with conventional human-driven vehicles (HVs), AVs have various potential benefits, such as increasing road capacity and lowering vehicular fuel consumption and emissions. Road infrastructure management, adaptation, and upgrade plays a key role in promoting the adoption and benefit realization of AVs.This dissertation investigated several strategic infrastructure planning problems for AVs. First, it studied the potential impact of AVs on the congestion patterns of transportation networks. Second, it investigated the strategic planning problem for a new form of managed lanes for autonomous vehicles, designated as autonomous-vehicle/toll lanes, which are freely accessible to autonomous vehicles while allowing human-driven vehicles to utilize the lanes by paying a toll.This new type of managed lanes has the potential of increasing traffic capacity and fully utilizing the traffic capacity by selling redundant road capacity to HVs. Last, this dissertation studied the strategic infrastructure planning problem for an infrastructure-enabled autonomous driving system. The system combines vehicles and infrastructure in the realization of autonomous driving. Equipped with roadside sensor and control systems, a regular road can be upgraded into an automated road providing autonomous driving service to vehicles. Vehicles only need to carry minimum required on-board devices to enable their autonomous driving on an automated road. The costs of vehicles can thus be significantly reduced. Advisors/Committee Members: Patrick Singleton, Michelle Mekker, Haitao Meng, ;.

Subjects/Keywords: Autonomous vehicles; Mixed traffic; Traffic assignment; User equilibrium; Dedicated autonomous vehicles lanes; Autonomous vehicle/toll lanes; Robust optimization; Strategic planning; Infrastructure-enabled autonomy; Congestion pricing; Civil and Environmental Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Liu, Z. (2020). Strategic Infrastructure Planning for Autonomous Vehicles. (Doctoral Dissertation). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/7734

Chicago Manual of Style (16th Edition):

Liu, Zhaocai. “Strategic Infrastructure Planning for Autonomous Vehicles.” 2020. Doctoral Dissertation, Utah State University. Accessed April 12, 2021. https://digitalcommons.usu.edu/etd/7734.

MLA Handbook (7th Edition):

Liu, Zhaocai. “Strategic Infrastructure Planning for Autonomous Vehicles.” 2020. Web. 12 Apr 2021.

Vancouver:

Liu Z. Strategic Infrastructure Planning for Autonomous Vehicles. [Internet] [Doctoral dissertation]. Utah State University; 2020. [cited 2021 Apr 12]. Available from: https://digitalcommons.usu.edu/etd/7734.

Council of Science Editors:

Liu Z. Strategic Infrastructure Planning for Autonomous Vehicles. [Doctoral Dissertation]. Utah State University; 2020. Available from: https://digitalcommons.usu.edu/etd/7734

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