Driver Assistance Eco-driving and Transmission Control with Deep Reinforcement Learning
With the growing need to reduce energy consumption and greenhouse gas emissions, Eco-driving strategies provide a significant opportunity for additional fuel savings on top of other technological solutions being pursued in the transportation sector. In this paper, a model-free deep reinforcement learning (RL) control agent is proposed for active Eco-driving assistance that trades-off fuel consumption against other driver-accommodation objectives, and learns optimal traction torque and transmission shifting policies from experience. The training scheme for the proposed RL agent uses an off-policy actor-critic architecture that iteratively does policy evaluation with a multi-step return and policy improvement with the maximum posteriori policy optimization algorithm for hybrid action spaces. The proposed Eco-driving RL agent is implemented on a commercial vehicle in car following traffic. It shows superior performance in minimizing fuel consumption compared to a baseline controller that has full knowledge of fuel-efficiency tables.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
Cyber Racing Coach: A Haptic Shared Control Framework for Teaching Advanced Driving Skills
This study introduces a haptic shared control framework designed to teach human drivers advanced driving skills. In this context, shared control refers to a driving mode where the human driver collaborates with an autono…
Autonomous DrivingReal-time Learning of Driving Gap Preference for Personalized Adaptive Cruise Control
Advanced Driver Assistance Systems (ADAS) are increasingly important in improving driving safety and comfort, with Adaptive Cruise Control (ACC) being one of the most widely used. However, pre-defined ACC settings may no…
Incremental LearningSafe Reinforcement Learning for an Energy-Efficient Driver Assistance System
Reinforcement learning (RL)-based driver assistance systems seek to improve fuel consumption via continual improvement of powertrain control actions considering experiential data from the field. However, the need to expl…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Safe Reinforcement LearningScene-Aware Conversational ADAS with Generative AI for Real-Time Driver Assistance
While autonomous driving technologies continue to advance, current Advanced Driver Assistance Systems (ADAS) remain limited in their ability to interpret scene context or engage with drivers through natural language. The…
Autonomous DrivingAn Open Case-based Reasoning Framework for Personalized On-board Driving Assistance in Risk Scenarios
Driver reaction is of vital importance in risk scenarios. Drivers can take correct evasive maneuver at proper cushion time to avoid the potential traffic crashes, but this reaction process is highly experience-dependent …