J
Julian Schlechtriemen
Researcher at Mercedes-Benz
Publications - 11
Citations - 383
Julian Schlechtriemen is an academic researcher from Mercedes-Benz. The author has contributed to research in topics: Advanced driver assistance systems & Architecture. The author has an hindex of 6, co-authored 11 publications receiving 262 citations. Previous affiliations of Julian Schlechtriemen include Daimler AG & University of Siegen.
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Proceedings ArticleDOI
A lane change detection approach using feature ranking with maximized predictive power
TL;DR: The novel approach is an extension of the Naïve Bayesian approach and results in a generative model that builds on the relations to the directly surrounding vehicles and to the static traffic environment.
Proceedings ArticleDOI
When will it change the lane? A probabilistic regression approach for rarely occurring events
TL;DR: This publication focuses on highway scenarios, where the maneuver space for traffic participants is limited to a small number of possible behavior classes, and extends approaches which solve the classification problem of lane-change behavior by introducing the novel aspect of estimating a continuous distribution of possible trajectories.
Proceedings ArticleDOI
Wiggling through complex traffic: Planning trajectories constrained by predictions
TL;DR: A novel approach to planning trajectories for autonomous vehicles by providing a flexible problem description and a trajectory planner without specialization to distinct classes of maneuvers beforehand, capable of considering multiple lanes including the predicted dynamics of other traffic participants, while being real-time capable at the same time.
Journal ArticleDOI
Teaching Vehicles to Anticipate: A Systematic Study on Probabilistic Behavior Prediction Using Large Data Sets
TL;DR: A systematic comparison of methods and strategies to generate intention for self-driving cars using machine learning techniques and it is shown that it is possible to classify driving maneuvers upcoming within the next 5 s with an Area Under the ROC Curve (AUC) above 0.92 for all defined maneuver classes.
Proceedings ArticleDOI
A probabilistic long term prediction approach for highway scenarios
TL;DR: This publication focuses on highway scenarios, where possible behaviors consist of changes in acceleration and lane-change maneuvers, and presents a novel approach for the prediction of future positions in highway scenarios.