What are the Differences in Driver Lane-Changing Intention Models Recognition Performance Between Connected and Non-Connected Environments
Author:
Affiliation:
1. School of Automotive Engineering, Shandong Jiaotong University, Jinan, China
2. School of Automobile, Chang’an University, Xi’an, China
3. School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, China
Funder
Key Laboratory of Transportation Industry of Automotive Transportation Safety Enhancement Technology, Chang’an University
Shandong Provincial Program of Introducing and Cultivating Talents of Discipline to Universities
Shandong Provincial Natural Science Foundation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6979/10659279/10423903.pdf?arnumber=10423903
Reference40 articles.
1. Turning Maneuver Prediction of Connected Vehicles at Signalized Intersections: A Dictionary Learning-Based Approach
2. An Ensemble Learning-Online Semi-Supervised Approach for Vehicle Behavior Recognition
3. Collaborative merging strategy for freeway ramp operations in a connected and autonomous vehicles environment
4. Influence of connected and autonomous vehicles on traffic flow stability and throughput
5. Stacking‐based ensemble learning method for the recognition of the preceding vehicle lane‐changing manoeuvre: A naturalistic driving study on the highway
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1. Lane change decision prediction: an efficient BO-XGB modelling approach with SHAP analysis;Transportmetrica A: Transport Science;2024-07-05
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