Graduation Year

2023

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Computer Science and Engineering

Major Professor

Srinivas Katkoori, Ph.D.

Committee Member

Hao Zheng, Ph.D.

Committee Member

Robert Karam, Ph.D.

Committee Member

Pei-Sung Lin, Ph.D.

Committee Member

Sisinnio Concas, Ph.D.

Committee Member

Kandethody M. Ramachandran, Ph.D.

Keywords

CARLA, Connected Autonomous Vehicles, Connected Vehicles, Simulation

Abstract

Connected Vehicles (CVs) make transportation safe by communicating with vehicles and the infrastructure in their neighborhood. CVs are embedded with onboard units (OBUs) which transmit basic safety messages (BSMs) containing the location, heading, and velocity information of the vehicle using either Dedicated Short-Range Communications (DSRC) or Cellular Vehicle-to-Everything (C-V2X) technology. These BSMs can be used to warn drivers using various vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) applications. Along with these applications, CV technology also gives rise to cooperative vehicular driving applications such as platooning. A group of vehicles can negotiate and drive jointly close to each other in a cooperative manner to form a platoon. Using connected and automated driving systems, platooning can aid in cutting total fuel costs, reducing CO2 emissions, improving efficiency, decreasing traffic congestion, increasing safety, and providing comfort for the drivers. Many existing approaches employ computer vision techniques which are costly in processing power and suffer from several drawbacks arising from poor visibility. Further, they assume a single-lane scenario which is over-simplifying.

This work proposes a novel approach that overcomes these limitations by leveraging only BSMs and no cameras. It can handle multiple lane scenarios as well which requires accurate determination of the relative position of CVs. The host vehicle implements the logic that computes the relative angle with the remote vehicle based on which hazardous conditions are detected and warned. Also, the same technique is employed for platooning where a vehicle that is interested in platooning can broadcast a DSRC message and interesting vehicles can establish communication to negotiate their route. Then, in a series of transactions over the DSRC channel, they can agree upon the leader position as well as the follower position(s). Once the negotiations are complete, a comprehensive platooning algorithm consisting of a platoon joiner and maintainer helps to form a platoon. Finally to evaluate these algorithms, `CARLA-Connect', a modular driving simulator framework for CVs and Connected Autonomous Vehicles (CAVs) is developed. Researchers and developers can use this framework to develop and test CV and CAV technology. To achieve this, CARLA, an open-source urban autonomous vehicle (AV) driving simulator framework is extended to support CV and CAV features. CARLA-Connect enables the continuous testing and development of CV and CAV algorithms to visually demonstrate their effectiveness in real-time simulation. Using CARLA-Connect, six V2V algorithms, three V2I algorithms, and platoon negotiation and formation algorithms are developed and validated. CARLA provides a pygame window where warnings can be displayed on the screen, simulating a human-machine interface (HMI). This is used to visually verify the warnings given to the driver and validate the underlying algorithms.

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