Conditional Generative Models for Dynamic Trajectory Generation and Urban Driving
This work explores methodologies for dynamic trajectory generation for urban driving
environments by utilizing coarse global plan representations. In contrast to state-of-the-art …
environments by utilizing coarse global plan representations. In contrast to state-of-the-art …
Tridentnetv2: Lightweight graphical global plan representations for dynamic trajectory generation
We present a framework for dynamic trajectory generation for autonomous navigation, which
does not rely on HD maps as the underlying representation. High Definition (HD) maps have …
does not rely on HD maps as the underlying representation. High Definition (HD) maps have …
Genad: Generative end-to-end autonomous driving
Directly producing planning results from raw sensors has been a long-desired solution for
autonomous driving and has attracted increasing attention recently. Most existing end-to …
autonomous driving and has attracted increasing attention recently. Most existing end-to …
OSM vs HD Maps: Map Representations for Trajectory Prediction
While High Definition (HD) Maps have long been favored for their precise depictions of static
road elements, their accessibility constraints and susceptibility to rapid environmental …
road elements, their accessibility constraints and susceptibility to rapid environmental …
Lanelet2 for nuscenes: Enabling spatial semantic relationships and diverse map-based anchor paths
A Naumann, F Hertlein, D Grimm… - Proceedings of the …, 2023 - openaccess.thecvf.com
Motion prediction and planning are key components to enable autonomous driving.
Although high definition (HD) maps provide important contextual information that constrains …
Although high definition (HD) maps provide important contextual information that constrains …
MFTraj: Map-Free, Behavior-Driven Trajectory Prediction for Autonomous Driving
This paper introduces a trajectory prediction model tailored for autonomous driving, focusing
on capturing complex interactions in dynamic traffic scenarios without reliance on high …
on capturing complex interactions in dynamic traffic scenarios without reliance on high …
End-to-end interpretable neural motion planner
In this paper, we propose a neural motion planner for learning to drive autonomously in
complex urban scenarios that include traffic-light handling, yielding, and interactions with …
complex urban scenarios that include traffic-light handling, yielding, and interactions with …
Residual Chain Prediction for Autonomous Driving Path Planning
L Zhou, Y Zhou, H Liu, A Knoll - arXiv preprint arXiv:2404.05423, 2024 - arxiv.org
In the rapidly evolving field of autonomous driving systems, the refinement of path planning
algorithms is paramount for navigating vehicles through dynamic environments, particularly …
algorithms is paramount for navigating vehicles through dynamic environments, particularly …
DriveSceneGen: Generating Diverse and Realistic Driving Scenarios from Scratch
Realistic and diverse traffic scenarios in large quantities are crucial for the development and
validation of autonomous driving systems. However, owing to numerous difficulties in the …
validation of autonomous driving systems. However, owing to numerous difficulties in the …
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 …