Traffic Sensory Data Classification by Quantifying Scenario Complexity

Abstract

For unmanned ground vehicle (UGV) off-line testing and performance evaluation, massive amount of traffic scenario data is often required. The annotations in current off-line traffic sensory dataset typically include I) types of roadways II) scene types III) specific characteristics that are generally considered challenging for cognitive algorithms. While such annotations are helpful in manual selection of data, they are insufficient for comprehensive and quantitate measurement of per-roadway-segment scenario complexity. To resolve such limitations, we propose a traffic sensory data classification paradigm based on quantifying the scenario complexity for each roadway segment, where such quantification is jointly based on road semantic complexity and traffic element complexity. The road semantic complexity is a proposed measurement of the complexity incurred by the static elements such as curvy roads, intersections, merges and splits, which is predicted with a Support Vector Regression (SVR). The traffic element complexity is a measurement of complexity due to dynamic traffic elements, such as nearby vehicles and pedestrians. Experimental results and a case study verify the efficacy of the proposed method.