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Daniel Bogdoll: Unterschied zwischen den Versionen

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<li>[https://aifb.kit.edu/web/Thema4948 Benchmarking Anomaly Detection on Camera and Lidar Data with 3D Voxel Representation][https://aifb.kit.edu/images/4/4e/2022_09_Benchmark_AnomalyDetection.pdf &nbsp;(PDF)]</li>
 
<li>[https://aifb.kit.edu/web/Thema4948 Benchmarking Anomaly Detection on Camera and Lidar Data with 3D Voxel Representation][https://aifb.kit.edu/images/4/4e/2022_09_Benchmark_AnomalyDetection.pdf &nbsp;(PDF)]</li>
<li>[https://aifb.kit.edu/web/Thema4944 Deep Learning World Models with Latent States for Autonomous Driving][https://aifb.kit.edu/images/5/5b/2022_09_World_Models.pdf &nbsp;(PDF)]</li>
 
 
<li>[https://aifb.kit.edu/web/Thema4949 Anomaly Detection with World Models for Autonomous Driving][https://aifb.kit.edu/images/2/20/2022_09_AnomalyDec_WorldModels.pdf &nbsp;(PDF)]</li>
 
<li>[https://aifb.kit.edu/web/Thema4949 Anomaly Detection with World Models for Autonomous Driving][https://aifb.kit.edu/images/2/20/2022_09_AnomalyDec_WorldModels.pdf &nbsp;(PDF)]</li>
<li>[https://aifb.kit.edu/web/Thema4947 Specialized Evaluation Metrics for Perception Tasks in Autonomous Driving][https://aifb.kit.edu/images/e/ea/2022_09_EvaluationMetrics.pdf &nbsp;(PDF)]</li>
 
 
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<li>[https://aifb.kit.edu/web/Thema4948 Benchmarking Anomaly Detection on Camera and Lidar Data with 3D Voxel Representation][https://aifb.kit.edu/images/4/4e/2022_09_Benchmark_AnomalyDetection.pdf &nbsp;(PDF)]</li>
 
<li>[https://aifb.kit.edu/web/Thema4948 Benchmarking Anomaly Detection on Camera and Lidar Data with 3D Voxel Representation][https://aifb.kit.edu/images/4/4e/2022_09_Benchmark_AnomalyDetection.pdf &nbsp;(PDF)]</li>
<li>[https://aifb.kit.edu/web/Thema4944 Deep Learning World Models with Latent States for Autonomous Driving][https://aifb.kit.edu/images/5/5b/2022_09_World_Models.pdf &nbsp;(PDF)]</li>
 
 
<li>[https://aifb.kit.edu/web/Thema4949 Anomaly Detection with World Models for Autonomous Driving][https://aifb.kit.edu/images/2/20/2022_09_AnomalyDec_WorldModels.pdf &nbsp;(PDF)]</li>
 
<li>[https://aifb.kit.edu/web/Thema4949 Anomaly Detection with World Models for Autonomous Driving][https://aifb.kit.edu/images/2/20/2022_09_AnomalyDec_WorldModels.pdf &nbsp;(PDF)]</li>
<li>[https://aifb.kit.edu/web/Thema4947 Specialized Evaluation Metrics for Perception Tasks in Autonomous Driving][https://aifb.kit.edu/images/e/ea/2022_09_EvaluationMetrics.pdf &nbsp;(PDF)]</li>
 
 
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Version vom 24. Januar 2023, 21:46 Uhr

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Hello, let me introduce myself :) I'm Daniel and I studied Computational Engineering Science at RWTH Aachen University from 2011 to 2019 with a focus on Machine Learning. In my theses, I worked on A*-based trajectory planning and LCSS-based route matching. In 2017, I completed a research stay in the Silicon Valley in the area of sensor data augmentation. Subsequently, in 2018, I founded the shared mobility startup SAYM.

Since November 2020, I am a research scientist at the FZI Research Center for Information Technology in the Technical Cognitive Systems (TKS) department and a PhD student at AIFB. My focus is on anomaly detection for autonomous vehicles. I am also deeply interested in the societal implications of autonomous vehicles in the (near) future.

Open Bachelor/Master Theses

  1. Benchmarking Anomaly Detection on Camera and Lidar Data with 3D Voxel Representation (PDF)
  2. Anomaly Detection with World Models for Autonomous Driving (PDF)


If you are interested in a student position in one of these fields, just send me an e-mail with your CV, your grades, and two sentences, why you are interested in the position. No cover letter necessary :)

Publications


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Forschungsgebiete
Deep Learning, Anomaly Detection