# Discrete and Continuous, Probabilistic Anticipation for Autonomous Robots in Urban Environments

arXiv: https://arxiv.org/abs/1309.0766 · HTML: https://mlsanity.com/paper/1309.0766/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2013.

## Built on by (most cited first, 2 of 2)

- [Exact Consistency Tests for Gaussian Mixture Filters using Normalized Deviation Squared Statistics](https://mlsanity.com/paper/2312.17420/) (2023)
- [V2V Spatiotemporal Interactive Pattern Recognition and Risk Analysis in Lane Changes](https://mlsanity.com/paper/2105.10688/) (2021)

## Abstract (arXiv)

This paper develops a probabilistic anticipation algorithm for dynamic objects observed by an autonomous robot in an urban environment. Predictive Gaussian mixture models are used due to their ability to probabilistically capture continuous and discrete obstacle decisions and behaviors; the predictive system uses the probabilistic output (state estimate and covariance) of a tracking system, and map of the environment to compute the probability distribution over future obstacle states for a specified anticipation horizon. A Gaussian splitting method is proposed based on the sigma-point transform and the nonlinear dynamics function, which enables increased accuracy as the number of mixands grows. An approach to caching elements of this optimal splitting method is proposed, in order to enable real-time implementation. Simulation results and evaluations on data from the research community demonstrate that the proposed algorithm can accurately anticipate the probability distributions over future states of nonlinear systems.