Adaptive Cruise Control
Adaptive Cruise Control (ACC) systems automatically adjust vehicle speed to maintain a safe following distance, enhancing driver comfort and safety. Current research emphasizes improving ACC robustness and adaptability through advanced machine learning models, including neural networks (like YOLO and LSTM) and normalizing flows, to handle diverse driving conditions, driver behaviors, and environmental factors such as shadows and inclement weather. These advancements aim to improve fuel efficiency, enhance safety by preventing collisions, and address privacy concerns related to data sharing in cooperative ACC systems. The resulting improvements in ACC technology have significant implications for autonomous driving and broader intelligent transportation systems.
Papers
A Novel Model for Driver Lane Change Prediction in Cooperative Adaptive Cruise Control Systems
Armin Nejadhossein Qasemabadi, Saeed Mozaffari, Mahdi Rezaei, Majid Ahmadi, Shahpour Alirezaee
LSTM-based Preceding Vehicle Behaviour Prediction during Aggressive Lane Change for ACC Application
Rajmeet Singh, Saeed Mozaffari, Mahdi Rezaei, Shahpour Alirezaee