Vision-based autonomous vehicle systems based on deep learning: A systematic literature review
In the past decade, autonomous vehicle systems (AVS) have advanced at an exponential
rate, particularly due to improvements in artificial intelligence, which have had a significant …
rate, particularly due to improvements in artificial intelligence, which have had a significant …
A survey on socially aware robot navigation: Taxonomy and future challenges
PT Singamaneni, P Bachiller-Burgos… - … Journal of Robotics …, 2024 - journals.sagepub.com
Socially aware robot navigation is gaining popularity with the increase in delivery and
assistive robots. The research is further fueled by a need for socially aware navigation skills …
assistive robots. The research is further fueled by a need for socially aware navigation skills …
How do autonomous vehicles decide?
The advancement in sensor technologies, mobile network technologies, and artificial
intelligence has pushed the boundaries of different verticals, eg, eHealth and autonomous …
intelligence has pushed the boundaries of different verticals, eg, eHealth and autonomous …
A reinforcement learning approach for enacting cautious behaviours in autonomous driving system: Safe speed choice in the interaction with distracted pedestrians
GPR Papini, A Plebe, M Da Lio… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
Driving requires the ability to handle unpredictable situations. Since it is not always possible
to predict an impending danger, a good driver should preventively assess whether a …
to predict an impending danger, a good driver should preventively assess whether a …
Modeling interactions of autonomous vehicles and pedestrians with deep multi-agent reinforcement learning for collision avoidance
R Trumpp, H Bayerlein… - 2022 IEEE Intelligent …, 2022 - ieeexplore.ieee.org
Reliable pedestrian crash avoidance mitigation (PCAM) systems are crucial components of
safe autonomous vehicles (AVs). The nature of the vehicle-pedestrian interaction where …
safe autonomous vehicles (AVs). The nature of the vehicle-pedestrian interaction where …
Hierarchical framework integrating rapidly-exploring random tree with deep reinforcement learning for autonomous vehicle
This paper proposes a systematic driving framework where the decision making module of
reinforcement learning (RL) is integrated with rapidly-exploring random tree (RRT) as …
reinforcement learning (RL) is integrated with rapidly-exploring random tree (RRT) as …
A study on an enhanced autonomous driving simulation model based on reinforcement learning using a collision prevention model
This paper set out to revise and improve existing autonomous driving models using
reinforcement learning, thus proposing a reinforced autonomous driving prediction model …
reinforcement learning, thus proposing a reinforced autonomous driving prediction model …
Human-Guided Deep Reinforcement Learning for Optimal Decision Making of Autonomous Vehicles
Although deep reinforcement learning (DRL) methods are promising for making behavioral
decisions in autonomous vehicles (AVs), their low training efficiency and difficulty to adapt to …
decisions in autonomous vehicles (AVs), their low training efficiency and difficulty to adapt to …
Navigation in urban environments amongst pedestrians using multi-objective deep reinforcement learning
N Deshpande, D Vaufreydaz… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
Urban autonomous driving in the presence of pedestrians as vulnerable road users is still a
challenging and less examined research problem. This work formulates navigation in urban …
challenging and less examined research problem. This work formulates navigation in urban …
Intention-Aware Decision-Making for Mixed Intersection Scenarios
This paper presents a white-box intention-aware decision-making for the handling of
interactions between a pedestrian and an automated vehicle (AV) in an unsignalized street …
interactions between a pedestrian and an automated vehicle (AV) in an unsignalized street …