Continuous decision‐making for autonomous driving at intersections using deep deterministic policy gradient
Intersections have been identified as the most complex and accident‐prone traffic scenarios
on road. Making appropriate decisions at intersections for driving safety, efficiency, and …
on road. Making appropriate decisions at intersections for driving safety, efficiency, and …
Behavioral decision‐making model of the intelligent vehicle based on driving risk assessment
Intelligent‐driving technologies play crucial roles in reducing road‐traffic accidents and
ensuring more convenience while driving. One of the significant challenges in developing …
ensuring more convenience while driving. One of the significant challenges in developing …
Towards socially responsive autonomous vehicles: A reinforcement learning framework with driving priors and coordination awareness
The advent of autonomous vehicles (AVs) alongside human-driven vehicles (HVs) has
ushered in an era of mixed traffic flow, presenting a significant challenge: the intricate …
ushered in an era of mixed traffic flow, presenting a significant challenge: the intricate …
Human-like decision-making of autonomous vehicles in dynamic traffic scenarios
With the maturation of autonomous driving technology, the use of autonomous vehicles in a
socially acceptable manner has become a growing demand of the public. Human-like …
socially acceptable manner has become a growing demand of the public. Human-like …
Human-like decision making for autonomous driving: A noncooperative game theoretic approach
Considering that human-driven vehicles and autonomous vehicles (AVs) will coexist on
roads in the future for a long time, how to merge AVs into human drivers' traffic ecology and …
roads in the future for a long time, how to merge AVs into human drivers' traffic ecology and …
An integrated model for autonomous speed and lane change decision-making based on deep reinforcement learning
The implementation of autonomous driving is inseparable from developing intelligent driving
decision-making models, which are facing high scene complexity, poor decision-making …
decision-making models, which are facing high scene complexity, poor decision-making …
Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions
V Bharilya, N Kumar - Vehicular Communications, 2024 - Elsevier
The significant contribution of human errors, accounting for approximately 94%(with a
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
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 …
The prevention dataset: a novel benchmark for prediction of vehicles intentions
Recent advances in autonomous driving have shown the importance of endowing self-
driving cars with the ability of predicting the intentions and future trajectories of other traffic …
driving cars with the ability of predicting the intentions and future trajectories of other traffic …
Deep reinforcement learning enabled decision-making for autonomous driving at intersections
Road intersection is one of the most complex and accident-prone traffic scenarios, so it's
challenging for autonomous vehicles (AVs) to make safe and efficient decisions at the …
challenging for autonomous vehicles (AVs) to make safe and efficient decisions at the …