Pedestrian prediction by planning using deep neural networks
Accurate traffic participant prediction is the prerequisite for collision avoidance of
autonomous vehicles. In this work, we propose to predict pedestrians using goal-directed …
autonomous vehicles. In this work, we propose to predict pedestrians using goal-directed …
End-to-end interactive prediction and planning with optical flow distillation for autonomous driving
With the recent advancement of deep learning technology, data-driven approaches for
autonomous car prediction and planning have achieved extraordinary performance …
autonomous car prediction and planning have achieved extraordinary performance …
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 …
What-if motion prediction for autonomous driving
Forecasting the long-term future motion of road actors is a core challenge to the deployment
of safe autonomous vehicles (AVs). Viable solutions must account for both the static …
of safe autonomous vehicles (AVs). Viable solutions must account for both the static …
Attentional-GCNN: Adaptive pedestrian trajectory prediction towards generic autonomous vehicle use cases
Autonomous vehicle navigation in shared pedestrian environments requires the ability to
predict future crowd motion both accurately and with minimal delay. Understanding the …
predict future crowd motion both accurately and with minimal delay. Understanding the …
Learning to predict vehicle trajectories with model-based planning
Predicting the future trajectories of on-road vehicles is critical for autonomous driving. In this
paper, we introduce a novel prediction framework called PRIME, which stands for Prediction …
paper, we introduce a novel prediction framework called PRIME, which stands for Prediction …
End-to-end intersection handling using multi-agent deep reinforcement learning
AP Capasso, P Maramotti, A Dell'Eva… - 2021 IEEE Intelligent …, 2021 - ieeexplore.ieee.org
Navigating through intersections is one of the main challenging tasks for an autonomous
vehicle. However, for the majority of intersections regulated by traffic lights, the problem …
vehicle. However, for the majority of intersections regulated by traffic lights, the problem …
Covernet: Multimodal behavior prediction using trajectory sets
T Phan-Minh, EC Grigore, FA Boulton… - Proceedings of the …, 2020 - openaccess.thecvf.com
We present CoverNet, a new method for multimodal, probabilistic trajectory prediction for
urban driving. Previous work has employed a variety of methods, including multimodal …
urban driving. Previous work has employed a variety of methods, including multimodal …
On exposing the challenging long tail in future prediction of traffic actors
Predicting the future states of dynamic traffic actors enables autonomous systems to avoid
accidents and operate safely. Remarkably, the most critical scenarios are much less …
accidents and operate safely. Remarkably, the most critical scenarios are much less …
Deeptrack: Lightweight deep learning for vehicle trajectory prediction in highways
Vehicle trajectory prediction is essential for enabling safety-critical intelligent transportation
systems (ITS) applications used in management and operations. While there have been …
systems (ITS) applications used in management and operations. While there have been …