Generalizing decision making for automated driving with an invariant environment representation using deep reinforcement learning
Data driven approaches for decision making applied to automated driving require
appropriate generalization strategies, to ensure applicability to the world's variability …
appropriate generalization strategies, to ensure applicability to the world's variability …
Multimodal trajectory predictions for autonomous driving without a detailed prior map
A Kawasaki, A Seki - Proceedings of the IEEE/CVF Winter …, 2021 - openaccess.thecvf.com
Predicting the future trajectories of surrounding vehicles is a key competence for safe and
efficient real-world autonomous driving systems. Previous works have presented deep …
efficient real-world autonomous driving systems. Previous works have presented deep …
Pip: Planning-informed trajectory prediction for autonomous driving
It is critical to predict the motion of surrounding vehicles for self-driving planning, especially
in a socially compliant and flexible way. However, future prediction is challenging due to the …
in a socially compliant and flexible way. However, future prediction is challenging due to the …
UST: Unifying spatio-temporal context for trajectory prediction in autonomous driving
Trajectory prediction has always been a challenging problem for autonomous driving, since
it needs to infer the latent intention from the behaviors and interactions from traffic …
it needs to infer the latent intention from the behaviors and interactions from traffic …
What Truly Matters in Trajectory Prediction for Autonomous Driving?
In the autonomous driving system, trajectory prediction plays a vital role in ensuring safety
and facilitating smooth navigation. However, we observe a substantial discrepancy between …
and facilitating smooth navigation. However, we observe a substantial discrepancy between …
A survey on deep-learning approaches for vehicle trajectory prediction in autonomous driving
With the rapid development of machine learning, autonomous driving has become a hot
issue, making urgent demands for more intelligent perception and planning systems. Self …
issue, making urgent demands for more intelligent perception and planning systems. Self …
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 …
Recup net: Recursive prediction network for surrounding vehicle trajectory prediction with future trajectory feedback
In order to predict the behavior of human drivers accurately, the autonomous vehicle should
be able to understand the reasoning and decision process of motion generation of human …
be able to understand the reasoning and decision process of motion generation of human …
Trajectory prediction for intelligent vehicles using spatial‐attention mechanism
It is of great interest for autonomous vehicles to predict the trajectory of other vehicles when
planning a safe trajectory. To accurately predict the trajectory of the target vehicle, the …
planning a safe trajectory. To accurately predict the trajectory of the target vehicle, the …
Prediction failure risk-aware decision-making for autonomous vehicles on signalized intersections
Motion prediction modules are crucial for autonomous vehicles to forecast the future
behavior of surrounding road users. Failures in prediction modules can mislead a …
behavior of surrounding road users. Failures in prediction modules can mislead a …