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 …
operating at different locations on a multilane corridor, which provides a platform to facilitate …
CAVBench: A benchmark suite for connected and autonomous vehicles
Connected and autonomous vehicles (CAVs) have recently attracted a significant amount of
attention both from researchers and industry. Numerous studies targeting algorithms …
attention both from researchers and industry. Numerous studies targeting algorithms …
Label efficient visual abstractions for autonomous driving
It is well known that semantic segmentation can be used as an effective intermediate
representation for learning driving policies. However, the task of street scene semantic …
representation for learning driving policies. However, the task of street scene semantic …
HiVeGPT: Human-machine-augmented intelligent vehicles with generative pre-trained transformer
Recently, a chat generative pre-trained transformer (ChatGPT) attracts widespread attention
in the academies and industries because of its powerful conversational ability with human …
in the academies and industries because of its powerful conversational ability with human …
Planning-oriented autonomous driving
Modern autonomous driving system is characterized as modular tasks in sequential order,
ie, perception, prediction, and planning. In order to perform a wide diversity of tasks and …
ie, perception, prediction, and planning. In order to perform a wide diversity of tasks and …
Flow: A modular learning framework for mixed autonomy traffic
The rapid development of autonomous vehicles (AVs) holds vast potential for transportation
systems through improved safety, efficiency, and access to mobility. However, the …
systems through improved safety, efficiency, and access to mobility. However, the …
Vehicle trajectory prediction using LSTMs with spatial–temporal attention mechanisms
Accurate vehicle trajectory prediction can benefit a variety of intelligent transportation system
applications ranging from traffic simulations to driver assistance. The need for this ability is …
applications ranging from traffic simulations to driver assistance. The need for this ability is …
Non-local social pooling for vehicle trajectory prediction
K Messaoud, I Yahiaoui… - 2019 IEEE Intelligent …, 2019 - ieeexplore.ieee.org
For an efficient integration of autonomous vehicles on roads, human-like reasoning and
decision making in complex traffic situations are needed. One of the key factors to achieve …
decision making in complex traffic situations are needed. One of the key factors to achieve …
Hierarchical vector transformer vehicle trajectories prediction with diffusion convolutional neural networks
In dynamic and interactive autonomous driving scenarios, accurately predicting the future
movements of vehicle agents is crucial. However, current methods often fail to capture …
movements of vehicle agents is crucial. However, current methods often fail to capture …
Federated deep learning meets autonomous vehicle perception: Design and verification
Realizing human-like perception is a challenge in open driving scenarios due to corner
cases and visual occlusions. To gather knowledge of rare and occluded instances …
cases and visual occlusions. To gather knowledge of rare and occluded instances …