Model-Based Derivation of Perception Accuracy Requirements for Vehicle Localization in Urban Environments

• Main Authors: Rohde, Jan, Stellet, Jan Erik, Mielenz, Holger, Zollner, J. Marius
• Format: Conference Proceeding
• Language: English
• Subjects: Accuracy; Automation; Derivation; Localization; Monte Carlo methods; Noise; Noise measurement; Perception; Position (location); Probabilistic logic; Probabilistic methods; Probability theory; Robot sensing systems; Vehicles
• ISSN: 2153-0009; 2153-0017
• DOI: 10.1109/ITSC.2015.121

In this contribution, we address the model-based derivation of perception requirements based on upper bounds on vehicle localization uncertainty for urban driver assistance (UDA) and urban automated driving (UAD). We show that a probabilistic model for the estimation of map-relative localization accuracy can be obtained and utilized for proper parametrization of a perception system. Therefore, the paper at hand entails two main contributions: i) Proposal of a probabilistic model for localization accuracy in closed form under the assumption of a generic measurement model with Gaussian noise and a stochastic landmark distribution, ii) Presentation of a framework for model-based derivation of perception requirements which permit desired localization performance. To exemplify the application of our method, sensor parameters for a stereo vision system (e.g. stereo base-width) are determined and verified via comprehensive simulation experiments. This is conducted in the context of an urban automated lane keeping system under explicit consideration of non-existent or occluded lane markings and curb stones.