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Autonomous vehicles enabled by the integration of IoT, edge intelligence, 5G, and blockchain
The wave of modernization around us has put the automotive industry on the brink of a
paradigm shift. Leveraging the ever-evolving technologies, vehicles are steadily …
paradigm shift. Leveraging the ever-evolving technologies, vehicles are steadily …
Decision-making driven by driver intelligence and environment reasoning for high-level autonomous vehicles: a survey
Autonomous vehicle (AV) is expected to reshape the future transportation system, and its
decision-making is one of the most critical modules. Many current decision-making modules …
decision-making is one of the most critical modules. Many current decision-making modules …
Graph-based topology reasoning for driving scenes
Understanding the road genome is essential to realize autonomous driving. This highly
intelligent problem contains two aspects-the connection relationship of lanes, and the …
intelligent problem contains two aspects-the connection relationship of lanes, and the …
Explainable AI for safe and trustworthy autonomous driving: a systematic review
Artificial Intelligence (AI) shows promising applications for the perception and planning tasks
in autonomous driving (AD) due to its superior performance compared to conventional …
in autonomous driving (AD) due to its superior performance compared to conventional …
Spatiotemporal scene-graph embedding for autonomous vehicle collision prediction
In autonomous vehicles (AVs), early warning systems rely on collision prediction to ensure
occupant safety. However, state-of-the-art methods using deep convolutional networks …
occupant safety. However, state-of-the-art methods using deep convolutional networks …
A survey of explainable graph neural networks: Taxonomy and evaluation metrics
Graph neural networks (GNNs) have demonstrated a significant boost in prediction
performance on graph data. At the same time, the predictions made by these models are …
performance on graph data. At the same time, the predictions made by these models are …
Vision-based traffic accident detection and anticipation: A survey
Traffic accident detection and anticipation is an obstinate road safety problem and
painstaking efforts have been devoted. With the rapid growth of video data, Vision-based …
painstaking efforts have been devoted. With the rapid growth of video data, Vision-based …
Applying machine learning and google street view to explore effects of drivers' visual environment on traffic safety
This study aims to explore the effects of drivers' visual environment on speeding crashes by
using different machine learning techniques. To obtain the data of drivers' visual …
using different machine learning techniques. To obtain the data of drivers' visual …
Trajectory prediction of seagoing ships in dynamic traffic scenes via a gated spatio-temporal graph aggregation network
Accurate ship trajectory prediction is essential in maritime traffic control and safety, requiring
the consideration of complex spatial and temporal dependencies within trajectory data. Most …
the consideration of complex spatial and temporal dependencies within trajectory data. Most …
Learning from interaction-enhanced scene graph for pedestrian collision risk assessment
Collision risk assessment aims to provide a subjective cognitive comprehension of the risk
level in driving scenarios, which is critical for the safety of autonomous driving systems …
level in driving scenarios, which is critical for the safety of autonomous driving systems …