All Weather Perception: Joint Data Association, Tracking, and Classification for Autonomous Ground Vehicles
By Peter Radecki, Murray Campbell, Kevin Matzen. 2016
34 citations recorded by the source corpus.
A novel probabilistic perception algorithm is presented as a real-time joint solution to data association, object tracking, and object classification for an autonomous ground vehicle in all-weather conditions. The presented algorithm extends a Rao-Blackwellized Particle Filter originally built with a particle filter for data association and a Kalman filter for multi-object tracking (Miller et al. 2011a) to now also include multiple model tracking for classification. Additionally a state-of-the-art vision detection algorithm that includes heading information for autonomous ground vehicle (AGV) applications was implemented. Cornell's AGV from the DARPA Urban Challenge was upgraded and used to experimentally examine if and how state-of-the-art vision algorithms can complement or replace lidar and radar sensors. Sensor and algorithm performance in adverse weather and lighting conditions is tested. Experimental evaluation demonstrates robust all-weather data association, tracking, and classification where camera, lidar, and radar sensors complement each other inside the joint probabilistic perception algorithm.
Subfields: Aerospace Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Oliver Bringmann
- Sumohana S. Channappayya
- Kazuya Takeda
- Matthias Bethge
- Alexander Carballo
- Ekim Yurtsever
- Wieland Brendel
- Harshitha Machiraju
In Artificial Intelligence
- Wieland Brendel
- Matthias Bethge
- Robert Geirhos
- Oliver Bringmann
- Evgenia Rusak
- Claudio Michaelis
- Kazuya Takeda
- Jacob Lambert
In Computer Vision and Pattern Recognition
- Matthias Bethge
- Wieland Brendel
- Robert Geirhos
- Luc Van Gool
- Trevor Darrell
- Andrea Vedaldi
- Ming-Hsuan Yang
- Claudio Michaelis
Citations in the corpus
- Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming (2019, background): “Radecki et al. [2016] experimentally test the performance of various sensors and object recognition and classification models in adverse weather and lighting conditions.”
- A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “A sensor fusion strategy is often employed to avoid any single point of failure [145].”
- An Evaluation Metric for Object Detection Algorithms in Autonomous Navigation Systems and its Application to a Real-Time Alerting System (2018, background): “The effects of weather conditions on the performance of sensors used for autonomous navigation have been studied in [4].”
- Artificial intelligence applications in the development of autonomous vehicles: a survey (2020, background): “[44] proposed to fuse data from cameras and LIDAR sensors and achieved better results under poor light conditions.”