Multimodal trajectory predictions for autonomous driving using deep convolutional networks
Autonomous driving presents one of the largest problems that the robotics and artificial
intelligence communities are facing at the moment, both in terms of difficulty and potential …
intelligence communities are facing at the moment, both in terms of difficulty and potential …
Maneuver-based trajectory prediction for self-driving cars using spatio-temporal convolutional networks
B Mersch, T Höllen, K Zhao… - 2021 IEEE/RSJ …, 2021 - ieeexplore.ieee.org
The ability to predict the future movements of other vehicles is a subconscious and effortless
skill for humans and key to safe autonomous driving. Therefore, trajectory prediction for …
skill for humans and key to safe autonomous driving. Therefore, trajectory prediction for …
Predicting motion of vulnerable road users using high-definition maps and efficient convnets
Following detection and tracking of traffic actors, prediction of their future motion is the next
critical component of a self-driving vehicle (SDV) technology, allowing the SDV to operate …
critical component of a self-driving vehicle (SDV) technology, allowing the SDV to operate …
Deep kinematic models for kinematically feasible vehicle trajectory predictions
Self-driving vehicles (SDVs) hold great potential for improving traffic safety and are poised to
positively affect the quality of life of millions of people. To unlock this potential one of the …
positively affect the quality of life of millions of people. To unlock this potential one of the …
Uncertainty-aware short-term motion prediction of traffic actors for autonomous driving
We address one of the crucial aspects necessary for safe and efficient operations of
autonomous vehicles, namely predicting future state of traffic actors in the autonomous …
autonomous vehicles, namely predicting future state of traffic actors in the autonomous …
A novel multimodal vehicle path prediction method based on temporal convolutional networks
MN Azadani, A Boukerche - IEEE Transactions on Intelligent …, 2022 - ieeexplore.ieee.org
Accurate and reliable prediction of future motions of the nearby agents and effective
environment understanding will contribute to high-quality and meticulous path planning for …
environment understanding will contribute to high-quality and meticulous path planning for …
Multi-modal motion prediction with transformer-based neural network for autonomous driving
Predicting the behaviors of other agents on the road is critical for autonomous driving to
ensure safety and efficiency. However, the challenging part is how to represent the social …
ensure safety and efficiency. However, the challenging part is how to represent the social …
Multiple trajectory prediction with deep temporal and spatial convolutional neural networks
Automated vehicles need to not only perceive their environment, but also predict the
possible future behavior of all detected traffic participants in order to safely navigate in …
possible future behavior of all detected traffic participants in order to safely navigate in …
Multimodal manoeuvre and trajectory prediction for automated driving on highways using transformer networks
S Mozaffari, MA Sormoli, K Koufos… - IEEE Robotics and …, 2023 - ieeexplore.ieee.org
Predicting the behaviour (ie, manoeuvre/trajectory) of other road users, including vehicles, is
critical for the safe and efficient operation of autonomous vehicles (AVs), aka, automated …
critical for the safe and efficient operation of autonomous vehicles (AVs), aka, automated …
Shared cross-modal trajectory prediction for autonomous driving
Predicting future trajectories of traffic agents in highly interactive environments is an
essential and challenging problem for the safe operation of autonomous driving systems. On …
essential and challenging problem for the safe operation of autonomous driving systems. On …