Paper Reading AI Learner

Modelling and Analysis of Car Following Algorithms for Fuel Economy Improvement in Connected and Autonomous Vehicles

2022-03-22 22:26:30
Ozgenur Kavas-Torris, Levent Guvenc

Abstract

Connectivity in ground vehicles allows vehicles to share crucial vehicle data, such as vehicle acceleration, with each other. Using sensors such as cameras, radars and lidars, on the other hand, the intravehicular distance between a leader vehicle and a host vehicle can be detected, as well as the relative speed. Cooperative Adaptive Cruise Control (CACC) builds upon ground vehicle connectivity and sensor information to form convoys with automated car following. CACC can also be used to improve fuel economy and mobility performance of vehicles in the said convoy. In this paper, 3 car following algorithms for fuel economy of CAVs are presented. An Adaptive Cruise Control (ACC) algorithm was designed as the benchmark model for comparison. A Cooperative Adaptive Cruise Control (CACC) was designed, which uses lead vehicle acceleration received through V2V in car following. an Ecological Cooperative Adaptive Cruise Control (Eco-CACC) model was developed that takes the erratic lead vehicle acceleration as a disturbance to be attenuated. A High Level (HL) controller was designed for decision making when the lead vehicle was an erratic driver. Model-in-the-Loop (MIL) and Hardware-in-the-Loop (HIL) simulations were run to test these car following algorithms for fuel economy performance. The results show that the HL controller was able to attain a smooth speed profile that consumed less fuel through using CACC and Eco-CACC than its ACC counterpart when the lead vehicle was erratic.

Abstract (translated)

URL

https://arxiv.org/abs/2203.12078

PDF

https://arxiv.org/pdf/2203.12078.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot