986 citations
- University of KaiserslauternDE26 papers
- Moscow Institute of Physics and TechnologyRU6 papers
- Rheinland-Pfälzische Technische Universität Kaiserslautern-LandauDE6 papers
- Universität UlmDE6 papers
- University of KasselDE6 papers
- Centre National de la Recherche ScientifiqueFR5 papers
- Karlsruhe Institute of TechnologyDE4 papers
- RWTH Aachen UniversityDE4 papers
- Technische Universität DarmstadtDE4 papers
- University of StuttgartDE4 papers
- Daimler Truck (Germany)3 papers
- ETH ZurichCH3 papers
6 papers · 2 filters
A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?
Mario Bijelic, Tobias Gruber, Werner Ritter
Autonomous driving at level five does not only means self-driving in the sunshine. Adverse weather is especially critical because fog, rain, and snow degrade the perception of the…
Slanted Stixels: A way to represent steep streets
Daniel Hernandez-Juarez, Lukas Schneider, Pau Cebrian +6
This work presents and evaluates a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather re…
Spatiotemporal Tile-based Attention-guided LSTMs for Traffic Video Prediction
Tu Nguyen
This extended abstract describes our solution for the Traffic4Cast Challenge 2019. The task requires modeling both fine-grained (pixel-level) and coarse (region-level) spatial stru…
Weather Influence and Classification with Automotive Lidar Sensors
Robin Heinzler, Philipp Schindler, Jürgen Seekircher +2
Lidar sensors are often used in mobile robots and autonomous vehicles to complement camera, radar and ultrasonic sensors for environment perception. Typically, perception algorithm…
Pixel-Accurate Depth Evaluation in Realistic Driving Scenarios
Tobias Gruber, Mario Bijelic, Felix Heide +2
This work introduces an evaluation benchmark for depth estimation and completion using high-resolution depth measurements with angular resolution of up to 25" (arcsecond), akin to…
Uncertainty Estimation in One-Stage Object Detection
Florian Kraus, Klaus Dietmayer
Environment perception is the task for intelligent vehicles on which all subsequent steps rely. A key part of perception is to safely detect other road users such as vehicles, pede…