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Found 15 papers, 2 papers with code
Human as AI Mentor: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous Driving
In this paradigm, the human expert serves as a mentor to the AI agent.
A Physics Enhanced Residual Learning (PERL) Framework for Vehicle Trajectory Prediction
PERL contains a physics model and a residual learning model.
Deep Reinforcement Learning Based Framework for Mobile Energy Disseminator Dispatching to Charge On-the-Road Electric Vehicles
In the first component of the framework, we develop a realistic reinforcement learning environment termed "ChargingEnv" which incorporates a reliable charging simulation system that accounts for common practical issues in wireless charging deployment, specifically, the charging panel misalignment.
Transfusor: Transformer Diffusor for Controllable Human-like Generation of Vehicle Lane Changing Trajectories
With ongoing development of autonomous driving systems and increasing desire for deployment, researchers continue to seek reliable approaches for ADS systems.
EPG-MGCN: Ego-Planning Guided Multi-Graph Convolutional Network for Heterogeneous Agent Trajectory Prediction
Then, the planning information of the ego vehicle is encoded by both the planning graph and the subsequent planning-guided prediction module to reduce uncertainty in the trajectory prediction.
Towards Safer Transportation: a self-supervised learning approach for traffic video deraining
Video monitoring of traffic is useful for traffic management and control, traffic counting, and traffic law enforcement.
Reason induced visual attention for explainable autonomous driving
Deep learning (DL) based computer vision (CV) models are generally considered as black boxes due to poor interpretability.
Scalable Traffic Signal Controls using Fog-Cloud Based Multiagent Reinforcement Learning
However, it has been computationally difficult to scale these solution approaches to large networks partly due to the curse of dimensionality that is encountered as the number of intersections increases.
Development and testing of an image transformer for explainable autonomous driving systems
The results demonstrate the efficacy of our proposed model as it exhibits superior performance (in terms of correct prediction of actions and explanations) compared to the benchmark model by a significant margin with lower computational cost.
Urban traffic dynamic rerouting framework: A DRL-based model with fog-cloud architecture
To address this question, this study proposes a two-stage model that combines GAQ (Graph Attention Network - Deep Q Learning) and EBkSP (Entropy Based k Shortest Path) using a fog-cloud architecture, to reroute vehicles in a dynamic urban environment and therefore to improve travel efficiency in terms of travel speed.
Using UAVs for vehicle tracking and collision risk assessment at intersections
Assessing collision risk is a critical challenge to effective traffic safety management.
Estimating IRI based on pavement distress type, density, and severity: Insights from machine learning techniques
However, it is costly to measure IRI, and for this reason, certain road classes are excluded from IRI measurements at a network level.
A DRL-based Multiagent Cooperative Control Framework for CAV Networks: a Graphic Convolution Q Network
Connected Autonomous Vehicle (CAV) Network can be defined as a collection of CAVs operating at different locations on a multilane corridor, which provides a platform to facilitate the dissemination of operational information as well as control instructions.
Leveraging the Capabilities of Connected and Autonomous Vehicles and Multi-Agent Reinforcement Learning to Mitigate Highway Bottleneck Congestion
In addressing this objective, we duly recognize that one of the main challenges of RL-based CAV controllers is the variety and complexity of inputs that exist in the real world, such as the information provided to the CAV by other connected entities and sensed information.
Facilitating Connected Autonomous Vehicle Operations Using Space-weighted Information Fusion and Deep Reinforcement Learning Based Control
From a general perspective, its implementation can provide guidance to connectivity equipment manufacturers and CAV operators, regarding the default CR settings for CAVs or the recommended CR setting in a given traffic environment.
