Rethinking integration of prediction and planning in deep learning-based automated driving systems: a review
Automated driving has the potential to revolutionize personal, public, and freight mobility.
Besides the enormous challenge of perception, ie accurately perceiving the environment …
Besides the enormous challenge of perception, ie accurately perceiving the environment …
Deep learning for vision-based prediction: A survey
A Rasouli - arXiv preprint arXiv:2007.00095, 2020 - arxiv.org
Vision-based prediction algorithms have a wide range of applications including autonomous
driving, surveillance, human-robot interaction, weather prediction. The objective of this …
driving, surveillance, human-robot interaction, weather prediction. The objective of this …
A survey on autonomous driving datasets: Data statistic, annotation, and outlook
Autonomous driving has rapidly developed and shown promising performance with recent
advances in hardware and deep learning methods. High-quality datasets are fundamental …
advances in hardware and deep learning methods. High-quality datasets are fundamental …
Transferable and adaptable driving behavior prediction
While autonomous vehicles still struggle to solve challenging situations during on-road
driving, humans have long mastered the essence of driving with efficient, transferable, and …
driving, humans have long mastered the essence of driving with efficient, transferable, and …
Vehicle trajectory prediction considering driver uncertainty and vehicle dynamics based on dynamic bayesian network
Vehicle trajectory prediction is a crucial but intricate problem for lateral driving assistance
systems because of driver uncertainty. This article presents a probabilistic vehicle-trajectory …
systems because of driver uncertainty. This article presents a probabilistic vehicle-trajectory …
Stopnet: Scalable trajectory and occupancy prediction for urban autonomous driving
We introduce a motion forecasting (behavior prediction) method that meets the latency
requirements for autonomous driving in dense urban environments without sacrificing …
requirements for autonomous driving in dense urban environments without sacrificing …
Socially-compatible behavior design of autonomous vehicles with verification on real human data
As more and more autonomous vehicles (AVs) are being deployed on public roads,
designing socially compatible behaviors for them is becoming increasingly important. In …
designing socially compatible behaviors for them is becoming increasingly important. In …
Predicting vehicles trajectories in urban scenarios with transformer networks and augmented information
A Quintanar, D Fernández-Llorca… - 2021 IEEE Intelligent …, 2021 - ieeexplore.ieee.org
Understanding the behavior of road users is of vital importance for the development of
trajectory prediction systems. In this context, the latest advances have focused on recurrent …
trajectory prediction systems. In this context, the latest advances have focused on recurrent …
A survey on autonomous driving datasets: Statistics, annotation quality, and a future outlook
Autonomous driving has rapidly developed and shown promising performance due to recent
advances in hardware and deep learning techniques. High-quality datasets are fundamental …
advances in hardware and deep learning techniques. High-quality datasets are fundamental …
Scenario factory: Creating safety-critical traffic scenarios for automated vehicles
M Klischat, EI Liu, F Holtke… - 2020 IEEE 23rd …, 2020 - ieeexplore.ieee.org
The safety validation of motion planning algorithms for automated vehicles requires a large
amount of data for virtual testing. Currently, this data is often collected through real test …
amount of data for virtual testing. Currently, this data is often collected through real test …