[HTML][HTML] Continual driver behaviour learning for connected vehicles and intelligent transportation systems: Framework, survey and challenges
Modelling, predicting and analysing driver behaviours are essential to advanced driver
assistance systems (ADAS) and the comprehensive understanding of complex driving …
assistance systems (ADAS) and the comprehensive understanding of complex driving …
Jfp: Joint future prediction with interactive multi-agent modeling for autonomous driving
Abstract We propose\textit {JFP}, a Joint Future Prediction model that can learn to generate
accurate and consistent multi-agent future trajectories. For this task, many different methods …
accurate and consistent multi-agent future trajectories. For this task, many different methods …
[HTML][HTML] Injecting knowledge in data-driven vehicle trajectory predictors
Vehicle trajectory prediction tasks have been commonly tackled from two distinct
perspectives: either with knowledge-driven methods or more recently with data-driven ones …
perspectives: either with knowledge-driven methods or more recently with data-driven ones …
SIND: A drone dataset at signalized intersection in China
Y Xu, W Shao, J Li, K Yang, W Wang… - 2022 IEEE 25th …, 2022 - ieeexplore.ieee.org
Intersection is one of the most challenging scenarios for autonomous driving tasks. Due to
the complexity and stochasticity, essential applications (eg, behavior modeling, motion …
the complexity and stochasticity, essential applications (eg, behavior modeling, motion …
Predictionnet: Real-time joint probabilistic traffic prediction for planning, control, and simulation
Predicting the future motion of traffic agents is crucial for safe and efficient autonomous
driving. To this end, we present PredictionNet, a deep neural network (DNN) that predicts …
driving. To this end, we present PredictionNet, a deep neural network (DNN) that predicts …
Vehicle trajectory prediction in connected environments via heterogeneous context-aware graph convolutional networks
The accurate trajectory prediction of surrounding vehicles is crucial for the sustainability and
safety of connected and autonomous vehicles under mixed traffic streams in the real world …
safety of connected and autonomous vehicles under mixed traffic streams in the real world …
Object recognition datasets and challenges: A review
Object recognition is among the fundamental tasks in the computer vision applications,
paving the path for all other image understanding operations. In every stage of progress in …
paving the path for all other image understanding operations. In every stage of progress in …
Game-theoretic modeling of traffic in unsignalized intersection network for autonomous vehicle control verification and validation
For a foreseeable future, autonomous vehicles (AVs) will operate in traffic together with
human-driven vehicles. Their planning and control systems need extensive testing …
human-driven vehicles. Their planning and control systems need extensive testing …
Graph and recurrent neural network-based vehicle trajectory prediction for highway driving
Integrating trajectory prediction to the decision-making and planning modules of modular
autonomous driving systems is expected to improve the safety and efficiency of self-driving …
autonomous driving systems is expected to improve the safety and efficiency of self-driving …
Hpnet: Dynamic trajectory forecasting with historical prediction attention
Predicting the trajectories of road agents is essential for autonomous driving systems. The
recent mainstream methods follow a static paradigm which predicts the future trajectory by …
recent mainstream methods follow a static paradigm which predicts the future trajectory by …