Search Results for author:
Found 6 papers, 2 papers with code
GraphAD: Interaction Scene Graph for End-to-end Autonomous Driving
With the representation of the ISG, the driving agents aggregate essential information from the most influential elements, including the road agents with potential collisions and the map elements to follow.
V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting
Utilizing infrastructure and vehicle-side information to track and forecast the behaviors of surrounding traffic participants can significantly improve decision-making and safety in autonomous driving.
Conditional Goal-oriented Trajectory Prediction for Interacting Vehicles with Vectorized Representation
Our CGTP framework is an end to end and interpretable model, including three main stages: context encoding, goal interactive prediction and trajectory interactive prediction.
A High Fidelity Simulation Framework for Potential Safety Benefits Estimation of Cooperative Pedestrian Perception
This paper proposes a high-fidelity simulation framework that can estimate the potential safety benefits of vehicle-to-infrastructure (V2I) pedestrian safety strategies.
Alpha-DAG: a reinforcement learning based algorithm to learn Directed Acyclic Graphs
Directed acyclic graphs (DAGs) are widely used to model the casual relationships among random variables in many disciplines.
Text Flow: A Unified Text Detection System in Natural Scene Images
With character candidates detected by cascade boosting, the min-cost flow network model integrates the last three sequential steps into a single process which solves the error accumulation problem at both character level and text line level effectively.
