Deep predictive autonomous driving using multi-agent joint trajectory prediction and traffic rules
Autonomous driving is a challenging problem because the autonomous vehicle must
understand complex and dynamic environment. This understanding consists of predicting …
understand complex and dynamic environment. This understanding consists of predicting …
Online vehicle trajectory prediction using policy anticipation network and optimization-based context reasoning
In this paper, we present an online two-level vehicle trajectory prediction framework for
urban autonomous driving where there are complex contextual factors, such as lane …
urban autonomous driving where there are complex contextual factors, such as lane …
Multi-agent driving behavior prediction across different scenarios with self-supervised domain knowledge
How to make precise multi-agent trajectory prediction is a crucial problem in the context of
autonomous driving. It is significant to have the ability to predict surrounding road …
autonomous driving. It is significant to have the ability to predict surrounding road …
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 …
Non-local social pooling for vehicle trajectory prediction
K Messaoud, I Yahiaoui… - 2019 IEEE Intelligent …, 2019 - ieeexplore.ieee.org
For an efficient integration of autonomous vehicles on roads, human-like reasoning and
decision making in complex traffic situations are needed. One of the key factors to achieve …
decision making in complex traffic situations are needed. One of the key factors to achieve …
Grip++: Enhanced graph-based interaction-aware trajectory prediction for autonomous driving
Despite the advancement in the technology of autonomous driving cars, the safety of a self-
driving car is still a challenging problem that has not been well studied. Motion prediction is …
driving car is still a challenging problem that has not been well studied. Motion prediction is …
Generic prediction architecture considering both rational and irrational driving behaviors
Accurately predicting future behaviors of surrounding vehicles is an essential capability for
autonomous vehicles in order to plan safe and feasible trajectories. The behaviors of others …
autonomous vehicles in order to plan safe and feasible trajectories. The behaviors of others …
Safe real-world autonomous driving by learning to predict and plan with a mixture of experts
The goal of autonomous vehicles is to navigate public roads safely and comfortably. To
enforce safety, traditional planning approaches rely on handcrafted rules to generate …
enforce safety, traditional planning approaches rely on handcrafted rules to generate …
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 …
Decoder fusion rnn: Context and interaction aware decoders for trajectory prediction
Forecasting the future behavior of all traffic agents in the vicinity is a key task to achieve safe
and reliable autonomous driving systems. It is a challenging problem as agents adjust their …
and reliable autonomous driving systems. It is a challenging problem as agents adjust their …