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Energy-Efficient Motion Planning and...

Joa, Eunhyek,

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Energy-Efficient Motion Planning and Control for Connected, Automated Vehicles /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Energy-Efficient Motion Planning and Control for Connected, Automated Vehicles // Eunhyek Joa.
作者:	Joa, Eunhyek,
面頁冊數:	1 electronic resource (163 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-09, Section: A.
Contained By:	Dissertations Abstracts International86-09A.
標題:	Automotive engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31766421
ISBN:	9798310102866

Energy-Efficient Motion Planning and Control for Connected, Automated Vehicles /
Joa, Eunhyek,

Energy-Efficient Motion Planning and Control for Connected, Automated Vehicles /Eunhyek Joa. - 1 electronic resource (163 pages)

Source: Dissertations Abstracts International, Volume: 86-09, Section: A.

As the demand for sustainable transportation solutions continues to rise, Connected Automated Vehicles (CAVs) present a promising opportunity to reduce energy consumption and environmental impact significantly. This dissertation addresses the challenge of developing energy-efficient motion planning and control strategies for CAVs.The research problem is to minimize total energy consumption while satisfying safety and input constraints for a given route. The key challenges are twofold. First, the long-horizon nature of the energy consumption minimization problem makes real-time computation difficult, as computational demands grow with the time horizon. Second, unlike fully autonomous vehicles equipped with precise localization systems such as LiDAR and high-definition maps, partial or conditionally automated vehicles rely on less precise localization methods, leading to meter-level localization uncertainty that introduces stochasticity to the optimal control problem. As a result, the problem is framed as a long-horizon stochastic optimal control problem, where both computational efficiency and addressing uncertainty are critical.To tackle these challenges, we present a data-driven approach to derive an approximate solution to the energy consumption minimization problem. We approach this by leveraging Model Predictive Control (MPC). Specifically, the long-horizon stochastic optimal control problem is approximated with the shorter-horizon optimal control problem, solved in a receding horizon manner. This reformulation makes the problem computationally tractable for real-time implementation. Additionally, to avoid myopic behavior due to the shorter horizon, we construct a value function using data-driven techniques and prove that as the dataset grows, the performance of our approximate solution converges to that of the long horizon stochastic optimal control problem. This is a key contribution, ensuring the approach remains effective while maintaining computational efficiency.We apply this method to the specific case of longitudinal motion planning for CAVs, focusing on scenarios where localization uncertainty is present. To validate the effectiveness of the proposed method, we conduct Vehicle-in-the-Loop (VIL) experiments, where a real vehicle is tested in an actual environment while the surrounding vehicles, traffic signals, and other elements of the driving scenario are simulated. These experiments demonstrate that our method achieves better energy efficiency compared to state-of-the-art algorithms designed for energy-efficient motion planning.Furthermore, we explore the potential for lateral motion planning to improve energy efficiency beyond what can be achieved through longitudinal motion planning alone. By considering lateral maneuvers such as lane changes, we show that additional energy savings are possible by validating through VIL experiments.

English

ISBN: 9798310102866Subjects--Topical Terms:

2181195
Automotive engineering.
Subjects--Index Terms:

Connected Automated Vehicles

Energy-Efficient Motion Planning and Control for Connected, Automated Vehicles /
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520 $a As the demand for sustainable transportation solutions continues to rise, Connected Automated Vehicles (CAVs) present a promising opportunity to reduce energy consumption and environmental impact significantly. This dissertation addresses the challenge of developing energy-efficient motion planning and control strategies for CAVs.The research problem is to minimize total energy consumption while satisfying safety and input constraints for a given route. The key challenges are twofold. First, the long-horizon nature of the energy consumption minimization problem makes real-time computation difficult, as computational demands grow with the time horizon. Second, unlike fully autonomous vehicles equipped with precise localization systems such as LiDAR and high-definition maps, partial or conditionally automated vehicles rely on less precise localization methods, leading to meter-level localization uncertainty that introduces stochasticity to the optimal control problem. As a result, the problem is framed as a long-horizon stochastic optimal control problem, where both computational efficiency and addressing uncertainty are critical.To tackle these challenges, we present a data-driven approach to derive an approximate solution to the energy consumption minimization problem. We approach this by leveraging Model Predictive Control (MPC). Specifically, the long-horizon stochastic optimal control problem is approximated with the shorter-horizon optimal control problem, solved in a receding horizon manner. This reformulation makes the problem computationally tractable for real-time implementation. Additionally, to avoid myopic behavior due to the shorter horizon, we construct a value function using data-driven techniques and prove that as the dataset grows, the performance of our approximate solution converges to that of the long horizon stochastic optimal control problem. This is a key contribution, ensuring the approach remains effective while maintaining computational efficiency.We apply this method to the specific case of longitudinal motion planning for CAVs, focusing on scenarios where localization uncertainty is present. To validate the effectiveness of the proposed method, we conduct Vehicle-in-the-Loop (VIL) experiments, where a real vehicle is tested in an actual environment while the surrounding vehicles, traffic signals, and other elements of the driving scenario are simulated. These experiments demonstrate that our method achieves better energy efficiency compared to state-of-the-art algorithms designed for energy-efficient motion planning.Furthermore, we explore the potential for lateral motion planning to improve energy efficiency beyond what can be achieved through longitudinal motion planning alone. By considering lateral maneuvers such as lane changes, we show that additional energy savings are possible by validating through VIL experiments.
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