Integrating Driving Behavior and Traffic Context Through Signal Symbolization for Data Reduction and Risky Lane Change Detection
By Ekim Yurtsever, Suguru Yamazaki, Chiyomi Miyajima, Kazuya Takeda, Masataka Mori, K. Hitomi, Masumi Egawa. 2018
31 citations recorded by the source corpus.
A novel method for integrating driving behavior and traffic context through signal symbolization is presented in this paper. This symbolization framework is proposed as a data reduction method for naturalistic driving studies. Continuous sensor signals have been converted and reduced into sequences of symbols (chunks) using a sticky hierarchical Dirichlet process hidden Markov model and a nested Pitman–Yor language model. Then, co-occurrence chunking (COOC), the proposed integration method, has been applied to the driver behavior and the traffic context chunks. After the integration, COOC chunks have been associated with prototype driving scenes by using latent Dirichlet allocation. Finally, the translated sequence of chunks has been clustered into groups. Risky lane change detection experiments have been conducted with the symbolized data for evaluation purposes. A dataset comprised of 988 lane change scenes has been utilized for this process. Co-occurrence chunking with clustering provided the best risky lane change detection.
Subfields: Artificial Intelligence, Automotive Engineering, Signal Processing
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
- Ekim Yurtsever
- Kazuya Takeda
- Jacob Lambert
- Alexander Carballo
- Baofu Wu
- John H. L. Hansen
- Chiyomi Miyajima
- Sidi Lu
In Automotive Engineering
- Ekim Yurtsever
- Jacob Lambert
- Alexander Carballo
- Kazuya Takeda
- Chiyomi Miyajima
- Baofu Wu
- Sidi Lu
- Ren Zhong
In Signal Processing
- Baofu Wu
- Kazuya Takeda
- Alexander Carballo
- Sidi Lu
- Ren Zhong
- Qingyan Zhang
- Yongtao Yao
- Ekim Yurtsever
Citations in the corpus
- A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “In [229]–[231] driving style was categorized into three and into two in [74], [209], [217], [218], [222].”
- A Traffic Flow Simulation Framework for Learning Driver Heterogeneity from Naturalistic Driving Data using Autoencoders (2019, background): “This progress led the development of machine-learning based models for traffic simulations (14)-(16) and driving behavior analysis (17)-(18) .”
- Computing Systems for Autonomous Driving: State of the Art and Challenges (2020, background): “get vehicle’s future behavior and detect unsafe lane change events [133], [134].”
- Risky Action Recognition in Lane Change Video Clips using Deep Spatiotemporal Networks with Segmentation Mask Transfer (2019, background): “on this dataset ignored the monocular camera footage and used ego-vehicle signals [16], [17].”