RESET: Revisiting Trajectory Sets for Conditional Behavior Prediction
- URL: http://arxiv.org/abs/2304.05856v1
- Date: Wed, 12 Apr 2023 13:39:09 GMT
- Title: RESET: Revisiting Trajectory Sets for Conditional Behavior Prediction
- Authors: Julian Schmidt, Pascal Huissel, Julian Wiederer, Julian Jordan,
Vasileios Belagiannis, Klaus Dietmayer
- Abstract summary: We propose RESET, which combines a new metric-driven algorithm for trajectory set generation with a graph-based encoder.
For unconditional prediction, RESET achieves comparable performance to a regression-based approach.
For conditional prediction, RESET achieves reasonable results with late fusion of the planned trajectory.
- Score: 12.945951957551317
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: It is desirable to predict the behavior of traffic participants conditioned
on different planned trajectories of the autonomous vehicle. This allows the
downstream planner to estimate the impact of its decisions. Recent approaches
for conditional behavior prediction rely on a regression decoder, meaning that
coordinates or polynomial coefficients are regressed. In this work we revisit
set-based trajectory prediction, where the probability of each trajectory in a
predefined trajectory set is determined by a classification model, and
first-time employ it to the task of conditional behavior prediction. We propose
RESET, which combines a new metric-driven algorithm for trajectory set
generation with a graph-based encoder. For unconditional prediction, RESET
achieves comparable performance to a regression-based approach. Due to the
nature of set-based approaches, it has the advantageous property of being able
to predict a flexible number of trajectories without influencing runtime or
complexity. For conditional prediction, RESET achieves reasonable results with
late fusion of the planned trajectory, which was not observed for
regression-based approaches before. This means that RESET is computationally
lightweight to combine with a planner that proposes multiple future plans of
the autonomous vehicle, as large parts of the forward pass can be reused.
Related papers
- Certified Human Trajectory Prediction [66.1736456453465]
Tray prediction plays an essential role in autonomous vehicles.
We propose a certification approach tailored for the task of trajectory prediction.
We address the inherent challenges associated with trajectory prediction, including unbounded outputs, and mutli-modality.
arXiv Detail & Related papers (2024-03-20T17:41:35Z) - PBP: Path-based Trajectory Prediction for Autonomous Driving [3.640190809479174]
Trajectory prediction plays a crucial role in the autonomous driving stack.
Goal-based prediction models have gained traction in recent years for addressing the multimodal nature of future trajectories.
arXiv Detail & Related papers (2023-09-07T14:45:41Z) - Uncertainty estimation of pedestrian future trajectory using Bayesian
approximation [137.00426219455116]
Under dynamic traffic scenarios, planning based on deterministic predictions is not trustworthy.
The authors propose to quantify uncertainty during forecasting using approximation which deterministic approaches fail to capture.
The effect of dropout weights and long-term prediction on future state uncertainty has been studied.
arXiv Detail & Related papers (2022-05-04T04:23:38Z) - Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion [88.45326906116165]
We present a new framework to formulate the trajectory prediction task as a reverse process of motion indeterminacy diffusion (MID)
We encode the history behavior information and the social interactions as a state embedding and devise a Transformer-based diffusion model to capture the temporal dependencies of trajectories.
Experiments on the human trajectory prediction benchmarks including the Stanford Drone and ETH/UCY datasets demonstrate the superiority of our method.
arXiv Detail & Related papers (2022-03-25T16:59:08Z) - Trajectory Forecasting from Detection with Uncertainty-Aware Motion
Encoding [121.66374635092097]
Trajectories obtained from object detection and tracking are inevitably noisy.
We propose a trajectory predictor directly based on detection results without relying on explicitly formed trajectories.
arXiv Detail & Related papers (2022-02-03T09:09:56Z) - Sliding Sequential CVAE with Time Variant Socially-aware Rethinking for
Trajectory Prediction [13.105275905781632]
Pedestrian trajectory prediction is a key technology in many applications such as video surveillance, social robot navigation, and autonomous driving.
This work proposes a novel trajectory prediction method called CSR, which consists of a cascaded conditional autoencoder (CVAE) module and a socially-aware regression module.
Experiments results demonstrate that the proposed method exhibits improvements over state-of-the-art method on the Stanford Drone dataset.
arXiv Detail & Related papers (2021-10-28T10:56:21Z) - Self-Supervised Action-Space Prediction for Automated Driving [0.0]
We present a novel learned multi-modal trajectory prediction architecture for automated driving.
It achieves kinematically feasible predictions by casting the learning problem into the space of accelerations and steering angles.
The proposed methods are evaluated on real-world datasets containing urban intersections and roundabouts.
arXiv Detail & Related papers (2021-09-21T08:27:56Z) - Learning to Predict Vehicle Trajectories with Model-based Planning [43.27767693429292]
We introduce a novel framework called PRIME, which stands for Prediction with Model-based Planning.
Unlike recent prediction works that utilize neural networks to model scene context, PRIME is designed to generate accurate and feasibility-guaranteed future trajectory predictions.
Our PRIME outperforms state-of-the-art methods in prediction accuracy, feasibility, and robustness under imperfect tracking.
arXiv Detail & Related papers (2021-03-06T04:49:24Z) - The Importance of Prior Knowledge in Precise Multimodal Prediction [71.74884391209955]
Roads have well defined geometries, topologies, and traffic rules.
In this paper we propose to incorporate structured priors as a loss function.
We demonstrate the effectiveness of our approach on real-world self-driving datasets.
arXiv Detail & Related papers (2020-06-04T03:56:11Z) - TPNet: Trajectory Proposal Network for Motion Prediction [81.28716372763128]
Trajectory Proposal Network (TPNet) is a novel two-stage motion prediction framework.
TPNet first generates a candidate set of future trajectories as hypothesis proposals, then makes the final predictions by classifying and refining the proposals.
Experiments on four large-scale trajectory prediction datasets, show that TPNet achieves the state-of-the-art results both quantitatively and qualitatively.
arXiv Detail & Related papers (2020-04-26T00:01:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.