Autonomous driving system: A comprehensive survey
Automation is increasingly at the forefront of transportation research, with the potential to
bring fully autonomous vehicles to our roads in the coming years. This comprehensive …
bring fully autonomous vehicles to our roads in the coming years. This comprehensive …
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 …
Artificial intelligence applications in the development of autonomous vehicles: A survey
The advancement of artificial intelligence (AI) has truly stimulated the development and
deployment of autonomous vehicles (AVs) in the transportation industry. Fueled by big data …
deployment of autonomous vehicles (AVs) in the transportation industry. Fueled by big data …
A survey of inverse reinforcement learning
Learning from demonstration, or imitation learning, is the process of learning to act in an
environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a …
environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a …
Collision avoidance for autonomous ship using deep reinforcement learning and prior-knowledge-based approximate representation
Reinforcement learning (RL) has shown superior performance in solving sequential
decision problems. In recent years, RL is gradually being used to solve unmanned driving …
decision problems. In recent years, RL is gradually being used to solve unmanned driving …
Follownet: a comprehensive benchmark for car-following behavior modeling
Car-following is a control process in which a following vehicle adjusts its acceleration to
keep a safe distance from the lead vehicle. Recently, there has been a booming of data …
keep a safe distance from the lead vehicle. Recently, there has been a booming of data …
Personalized car following for autonomous driving with inverse reinforcement learning
Driving automation is gradually replacing human driving maneuvers in different applications
such as adaptive cruise control and lane keeping. However, contemporary driving …
such as adaptive cruise control and lane keeping. However, contemporary driving …
A reinforcement learning framework for video frame-based autonomous car-following
Car-following theory has received considerable attention as a core component of Intelligent
Transportation Systems. However, its application to the emerging autonomous vehicles …
Transportation Systems. However, its application to the emerging autonomous vehicles …
Path tracking of autonomous vehicle based on adaptive model predictive control
F Lin, Y Chen, Y Zhao, S Wang - International Journal of …, 2019 - journals.sagepub.com
In most cases, a vehicle works in a complex environment, with working conditions changing
frequently. For most model predictive tracking controllers, however, the impacts of some …
frequently. For most model predictive tracking controllers, however, the impacts of some …
Learning the Car‐following Behavior of Drivers Using Maximum Entropy Deep Inverse Reinforcement Learning
Y Zhou, R Fu, C Wang - Journal of advanced transportation, 2020 - Wiley Online Library
The present study proposes a framework for learning the car‐following behavior of drivers
based on maximum entropy deep inverse reinforcement learning. The proposed framework …
based on maximum entropy deep inverse reinforcement learning. The proposed framework …