Precise and efficient model-based vehicle tracking method using Rao-Blackwellized and scaling series particle filters
By Mengwen He, Eijiro Takeuchi, Yoshiki Ninomiya, Shinpei Kato. 2016
13 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Safety, Risk, Reliability and Quality
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Kazuya Takeda
- Jacob Lambert
- Alexander Carballo
- Ekim Yurtsever
- Thiago Oliveira-Santos
- A. D. Souza
- Rodrigo Berriel
- Filipe Wall Mutz
In Computer Vision and Pattern Recognition
- Ekim Yurtsever
- Alexander Carballo
- Jacob Lambert
- Vinicius B. Cardoso
- Thiago Oliveira-Santos
- Raphael V. Carneiro
- Pedro Azevedo
- A. D. Souza
In Safety, Risk, Reliability and Quality
Citations in the corpus
- A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “More involved filtering methods such as RaoBlackwellized particle filters have also been used to keep track of both dynamic variables and vehicle geometry variables for an L-shape vehicle model [189].”
- Self-Driving Cars: A Survey (2019, methodology): “The work of Petrovskaya and Thrun [PET09] was revised by He et al. [HE16] that propose to combine RBPF with Scaling Series Particle Filter (SSPF) for geometry fitting and for motion estimate throughout the entire tracking process.”