Driver modeling through deep reinforcement learning and behavioral game theory
In this work, a synergistic combination of deep reinforcement learning and hierarchical game
theory is proposed as a modeling framework for behavioral predictions of drivers in highway …
theory is proposed as a modeling framework for behavioral predictions of drivers in highway …
Microscopic traffic simulation by cooperative multi-agent deep reinforcement learning
G Bacchiani, D Molinari, M Patander - arXiv preprint arXiv:1903.01365, 2019 - arxiv.org
Expert human drivers perform actions relying on traffic laws and their previous experience.
While traffic laws are easily embedded into an artificial brain, modeling human complex …
While traffic laws are easily embedded into an artificial brain, modeling human complex …
Human-like autonomous car-following model with deep reinforcement learning
This study proposes a framework for human-like autonomous car-following planning based
on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation …
on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation …
Modified DDPG car-following model with a real-world human driving experience with CARLA simulator
In the autonomous driving field, fusion of human knowledge into Deep Reinforcement
Learning (DRL) is often based on the human demonstration recorded in a simulated …
Learning (DRL) is often based on the human demonstration recorded in a simulated …
Prediction based decision making for autonomous highway driving
Autonomous driving decision-making is a challenging task due to the inherent complexity
and uncertainty in traffic. For example, adjacent vehicles may change their lane or overtake …
and uncertainty in traffic. For example, adjacent vehicles may change their lane or overtake …
Deep reinforcement learning for personalized driving recommendations to mitigate aggressiveness and riskiness: Modeling and impact assessment
EG Mantouka, EI Vlahogianni - Transportation research part C: emerging …, 2022 - Elsevier
Most driving recommendation and assistance systems, such as Advanced Driving
Assistance Systems (ADAS), are usually designed based on the behavior of an average …
Assistance Systems (ADAS), are usually designed based on the behavior of an average …
Unified automatic control of vehicular systems with reinforcement learning
Emerging vehicular systems with increasing proportions of automated components present
opportunities for optimal control to mitigate congestion and increase efficiency. There has …
opportunities for optimal control to mitigate congestion and increase efficiency. There has …
Automated vehicle's behavior decision making using deep reinforcement learning and high-fidelity simulation environment
Automated vehicles (AVs) are deemed to be the key element for the intelligent transportation
system in the future. Many studies have been made to improve AVs' ability of environment …
system in the future. Many studies have been made to improve AVs' ability of environment …
Toward learning human-like, safe and comfortable car-following policies with a novel deep reinforcement learning approach
In this paper, we present an advanced adaptive cruise control (ACC) concept powered by
Deep Reinforcement Learning (DRL) that generates safe, human-like, and comfortable car …
Deep Reinforcement Learning (DRL) that generates safe, human-like, and comfortable car …
Driver behavior modeling toward autonomous vehicles: Comprehensive review
Driver behavior models have been used as input to self-coaching, accident prevention
studies, and developing driver-assisting systems. In recent years, driver behavior …
studies, and developing driver-assisting systems. In recent years, driver behavior …