Long-term prediction of vehicle behavior using short-term uncertainty-aware trajectories and high-definition maps
Motion prediction of surrounding vehicles is one of the most important tasks handled by a
self-driving vehicle, and represents a critical step in the autonomous system necessary to …
self-driving vehicle, and represents a critical step in the autonomous system necessary to …
Differentiable integrated motion prediction and planning with learnable cost function for autonomous driving
Predicting the future states of surrounding traffic participants and planning a safe, smooth,
and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs) …
and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs) …
Multi-head attention based probabilistic vehicle trajectory prediction
This paper presents online-capable deep learning model for probabilistic vehicle trajectory
prediction. We propose a simple encoder-decoder architecture based on multihead …
prediction. We propose a simple encoder-decoder architecture based on multihead …
Lane-attention: Predicting vehicles' moving trajectories by learning their attention over lanes
Accurately forecasting the future movements of surrounding vehicles is essential for safe
and efficient operations of autonomous driving cars. This task is difficult because a vehicle's …
and efficient operations of autonomous driving cars. This task is difficult because a vehicle's …
Multimodal trajectory predictions for urban environments using geometric relationships between a vehicle and lanes
A Kawasaki, A Seki - 2020 IEEE International Conference on …, 2020 - ieeexplore.ieee.org
Implementation of safe and efficient autonomous driving systems requires accurate
prediction of the long-term trajectories of surrounding vehicles. High uncertainty in traffic …
prediction of the long-term trajectories of surrounding vehicles. High uncertainty in traffic …
Multi-modal trajectory prediction for autonomous driving with semantic map and dynamic graph attention network
B Dong, H Liu, Y Bai, J Lin, Z Xu, X Xu… - arXiv preprint arXiv …, 2021 - arxiv.org
Predicting future trajectories of surrounding obstacles is a crucial task for autonomous
driving cars to achieve a high degree of road safety. There are several challenges in …
driving cars to achieve a high degree of road safety. There are several challenges in …
Vehicle trajectory prediction at intersections using interaction based generative adversarial networks
Vehicle trajectory prediction at intersections is both essential and challenging for
autonomous vehicle navigation. This problem is aggravated when the traffic is …
autonomous vehicle navigation. This problem is aggravated when the traffic is …
Probabilistic multi-modal trajectory prediction with lane attention for autonomous vehicles
Trajectory prediction is crucial for autonomous vehicles. The planning system not only needs
to know the current state of the surrounding objects but also their possible states in the …
to know the current state of the surrounding objects but also their possible states in the …
Trajectory prediction for autonomous driving based on multi-head attention with joint agent-map representation
Predicting the trajectories of surrounding agents is an essential ability for autonomous
vehicles navigating through complex traffic scenes. The future trajectories of agents can be …
vehicles navigating through complex traffic scenes. The future trajectories of agents can be …
Integrating deep reinforcement learning with model-based path planners for automated driving
Automated driving in urban settings is challenging. Human participant behavior is difficult to
model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when …
model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when …