Search Results for author: Monika Sester

Found 13 papers, 7 papers with code

Controllable Diverse Sampling for Diffusion Based Motion Behavior Forecasting

no code implementations6 Feb 2024 Yiming Xu, Hao Cheng, Monika Sester

These issues lead the existing methods to a loss of predictive diversity and adherence to the scene constraints.

Autonomous Driving Denoising +1

LAformer: Trajectory Prediction for Autonomous Driving with Lane-Aware Scene Constraints

1 code implementation27 Feb 2023 Mengmeng Liu, Hao Cheng, Lin Chen, Hellward Broszio, Jiangtao Li, Runjiang Zhao, Monika Sester, Michael Ying Yang

Trajectory prediction for autonomous driving must continuously reason the motion stochasticity of road agents and comply with scene constraints.

Autonomous Driving Trajectory Prediction

ForceFormer: Exploring Social Force and Transformer for Pedestrian Trajectory Prediction

no code implementations15 Feb 2023 Weicheng Zhang, Hao Cheng, Fatema T. Johora, Monika Sester

Predicting trajectories of pedestrians based on goal information in highly interactive scenes is a crucial step toward Intelligent Transportation Systems and Autonomous Driving.

Autonomous Driving Pedestrian Trajectory Prediction +1

Generating Evidential BEV Maps in Continuous Driving Space

1 code implementation6 Feb 2023 Yunshuang Yuan, Hao Cheng, Michael Ying Yang, Monika Sester

Safety is critical for autonomous driving, and one aspect of improving safety is to accurately capture the uncertainties of the perception system, especially knowing the unknown.

Autonomous Driving object-detection +2

GATraj: A Graph- and Attention-based Multi-Agent Trajectory Prediction Model

1 code implementation16 Sep 2022 Hao Cheng, Mengmeng Liu, Lin Chen, Hellward Broszio, Monika Sester, Michael Ying Yang

This paper proposes an attention-based graph model, named GATraj, which achieves a good balance of prediction accuracy and inference speed.

Autonomous Driving Robot Navigation +1

Determination of building flood risk maps from LiDAR mobile mapping data

no code implementations14 Jan 2022 Yu Feng, Qing Xiao, Claus Brenner, Aaron Peche, Juntao Yang, Udo Feuerhake, Monika Sester

By comparing the detected facade openings' heights with the predicted water levels from a flood simulation model, a map can be produced which assigns per-building flood risk levels.

object-detection Object Detection

Keypoints-Based Deep Feature Fusion for Cooperative Vehicle Detection of Autonomous Driving

1 code implementation23 Sep 2021 Yunshuang Yuan, Hao Cheng, Monika Sester

Sharing collective perception messages (CPM) between vehicles is investigated to decrease occlusions so as to improve the perception accuracy and safety of autonomous driving.

Autonomous Driving

Interaction Detection Between Vehicles and Vulnerable Road Users: A Deep Generative Approach with Attention

no code implementations9 May 2021 Hao Cheng, Li Feng, Hailong Liu, Takatsugu Hirayama, Hiroshi Murase, Monika Sester

Intersections where vehicles are permitted to turn and interact with vulnerable road users (VRUs) like pedestrians and cyclists are among some of the most challenging locations for automated and accurate recognition of road users' behavior.

Optical Flow Estimation Self-Driving Cars

Exploring Dynamic Context for Multi-path Trajectory Prediction

2 code implementations30 Oct 2020 Hao Cheng, Wentong Liao, Xuejiao Tang, Michael Ying Yang, Monika Sester, Bodo Rosenhahn

In our framework, first, the spatial context between agents is explored by using self-attention architectures.

Trajectory Forecasting

Flood severity mapping from Volunteered Geographic Information by interpreting water level from images containing people: a case study of Hurricane Harvey

no code implementations21 Jun 2020 Yu Feng, Claus Brenner, Monika Sester

Since more images are shared on social media than ever before, recent research focused on the extraction of flood-related posts by analyzing images in addition to texts.

AMENet: Attentive Maps Encoder Network for Trajectory Prediction

1 code implementation15 Jun 2020 Hao Cheng, Wentong Liao, Michael Ying Yang, Bodo Rosenhahn, Monika Sester

Trajectory prediction is critical for applications of planning safe future movements and remains challenging even for the next few seconds in urban mixed traffic.

Trajectory Prediction

MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic

1 code implementation14 Feb 2020 Hao Cheng, Wentong Liao, Michael Ying Yang, Monika Sester, Bodo Rosenhahn

In inference time, we combine the past context and motion information of the target agent with samplings of the latent variables to predict multiple realistic trajectories in the future.

Autonomous Driving Intent Detection +1

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