Identifying and Assessing Research Gaps for Energy Efficient Control of Electrified Autonomous Vehicle Eco-Driving

Araghi, Farhang Motallebi; Rabinwoitz, Aaron; Ang, Chon Chia; Sharma, Sachin; Kadav, Parth; Meyer, Richard T.; Bradley, Thomas; Asher, Zachary D.

doi:10.1007/978-3-031-28016-0_27

Cited by 3 publications

References 84 publications

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A novel ML-MCDM-based decision support system for evaluating autonomous vehicle integration scenarios in Geneva’s public transportation

Zakeri,

Konstantas,

Sorooshian

et al. 2024

Artif Intell Rev

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This paper proposes a novel decision-support system (DSS) to assist decision-makers in the ULTIMO project with integrating Autonomous Vehicles (AVs) in Geneva, Switzerland. Specifically, it aids in selecting the best scenario for incorporating AVs into Geneva’s public transportation system. The proposed DSS is architected on a combined integrated framework that includes a machine learning (ML) algorithm, random forest (RF) algorithm, and three novel multi-criteria decision-making (MCDM) algorithms: (1) Modified E-ARWEN (ME-ARWEN) for selecting the best scenario with high sensitivity; (2) Compromiser—Positive, Neutral, Negative (Compromiser-PNN) for extracting weights from stakeholders, considering their preferences and potential conflicts; and (3) Collective Weight Processor (CWP) for deriving weights from expert opinions. Besides the main objective, this article also aims to: (1) Address the gap in practical DSS software within AV-related studies by providing Python codes of the DSS; (2) Develop a highly sensitive and comprehensive MCDM framework to address the project’s needs; and (3) Employ Artificial Intelligence within the DSS to optimize outputs. By the application of the proposed DSS, four scenarios were evaluated: (1) Full integration of AVs; (2) Partial integration; (3) Pilot project in limited areas; and (4) Delayed integration. The analysis identified partial integration as the best scenario for integrating AVs. Furthermore, comprehensive analyses conducted to validate the DSS outputs demonstrated the reliability of the results.

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A novel ML-MCDM-based decision support system for evaluating autonomous vehicle integration scenarios in Geneva’s public transportation

Zakeri,

Konstantas,

Sorooshian

et al. 2024

Artif Intell Rev

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Autonomous Eco-Driving Evaluation of an Electric Vehicle on a Chassis Dynamometer

Motallebiaraghi¹,

Rabinowitz²,

Rojas³

et al. 2023

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">Connected and Automated Vehicles (CAV) provide new prospects for energy-efficient driving due to their improved information accessibility, enhanced processing capacity, and precise control. The idea of the Eco-Driving (ED) control problem is to perform energy-efficient speed planning for a connected and automated vehicle using data obtained from high-resolution maps and Vehicle-to-Everything (V2X) communication. With the recent goal of commercialization of autonomous vehicle technology, more research has been done to the investigation of autonomous eco-driving control. Previous research for autonomous eco-driving control has shown that energy efficiency improvements can be achieved by using optimization techniques. Most of these studies are conducted through simulations, but many more physical vehicle integrated test application studies are needed. This paper addresses this research gap by highlighting the Vehicle Hardware-In-the-Loop (VHIL) energy saving potential of autonomous eco-driving control for connected and automated vehicles. A comprehensive system description of autonomous eco-driving control is presented by describing subsystems and their functionalities. Validated autonomous eco-driving optimization methods, including Dynamic Programming (DP), Genetic Algorithms (GA), and Particle Swarm Optimization (PSO) were tested with a control-enabled electric Kia Soul using a 2-wheel-drive chassis dynamometer. VHIL test performance of these methods is evaluated relative to each other as well as a baseline scenario. The conclusions were derived from examinations that were carried out on a chassis dynamometer. The results show that energy efficiency may be enhanced by anywhere from 5 to 15 %, depending on the method that is used. When compared to our earlier simulation results, it is demonstrated that the VHIL outcomes achieve the predicted gain in energy efficiency. The overall results show that the use of the dynamic programming method is the most effective strategy for enhancing energy efficiency. It is shown that the application of methods that are derived from genetic algorithms has the potential to increase energy efficiency when integrated in the test vehicle.</div></div>

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Vehicle Lateral Offset Estimation Using Infrastructure Information for Reduced Compute Load

Sharma¹,

Rojas²,

Ekti³

et al. 2023

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">Accurate perception of the driving environment and a highly accurate position of the vehicle are paramount to safe Autonomous Vehicle (AV) operation. AVs gather data about the environment using various sensors. For a robust perception and localization system, incoming data from multiple sensors is usually fused together using advanced computational algorithms, which historically requires a high-compute load. To reduce AV compute load and its negative effects on vehicle energy efficiency, we propose a new infrastructure information source (IIS) to provide environmental data to the AV. The new energy–efficient IIS, chip–enabled raised pavement markers are mounted along road lane lines and are able to communicate a unique identifier and their global navigation satellite system position to the AV. This new IIS is incorporated into an energy efficient sensor fusion strategy that combines its information with that from traditional sensor. IIS reduce the need for camera imaging, image processing, and LIDAR use and point cloud processing. We show that IIS, when combined with traditional sensors, results in more accurate perception and localization outcomes and a reduced AV compute load.</div></div>

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Identifying and Assessing Research Gaps for Energy Efficient Control of Electrified Autonomous Vehicle Eco-Driving

Cited by 3 publications

References 84 publications

A novel ML-MCDM-based decision support system for evaluating autonomous vehicle integration scenarios in Geneva’s public transportation

A novel ML-MCDM-based decision support system for evaluating autonomous vehicle integration scenarios in Geneva’s public transportation

Autonomous Eco-Driving Evaluation of an Electric Vehicle on a Chassis Dynamometer

Vehicle Lateral Offset Estimation Using Infrastructure Information for Reduced Compute Load

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