Using road topology to improve cyclist path prediction

@article{Pool2017UsingRT,
  title={Using road topology to improve cyclist path prediction},
  author={Ewoud A. I. Pool and Julian F. P. Kooij and Dariu Gavrila},
  journal={2017 IEEE Intelligent Vehicles Symposium (IV)},
  year={2017},
  pages={289-296}
}
We learn motion models for cyclist path prediction on real-world tracks obtained from a moving vehicle, and propose to exploit the local road topology to obtain better predictive distributions. The tracks are extracted from the Tsinghua-Daimler Cyclist Benchmark for cyclist detection, and corrected for vehicle egomotion. Tracks are then spatially aligned to local curves and crossings in the road. We study a standard approach for path prediction in the literature based on Kalman Filters, as well… CONTINUE READING

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Key Quantitative Results

  • Our experiments demonstrate an improved prediction accuracy (up to 20% on sharp turns) of mixing specialized motion models for canonical directions, and prior knowledge on the road topology.

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