TY  - JOUR
AB  - Purpose Feature selection is crucial for machine learning to recognize lane-change (LC) maneuver as there exist a large number of feature candidates. Blindly using feature could take up large storage and excessive computation time, while insufficient feature selection would cause poor performance. Selecting high contributive features to classify LC and lane-keep behavior is effective for maneuver recognition. This paper aims to propose a feature selection method from a statistical view based on an analysis from naturalistic driving data.Design/methodology/approach In total, 1,375 LC cases are analyzed. To comprehensively select features, the authors extract the feature candidates from both time and frequency domains with various LC scenarios segmented by an occupancy schedule grid. Then the effect size (Cohen’s d) and p-value of every feature are computed to assess their contribution for each scenario.Findings It has been found that the common lateral features, e.g. yaw rate, lateral acceleration and time-to-lane crossing, are not strong features for recognition of LC maneuver as empirical knowledge. Finally, cross-validation tests are conducted to evaluate model performance using metrics of receiver operating characteristic. Experimental results show that the selected features can achieve better recognition performance than using all the features without purification.Originality/value In this paper, the authors investigate the contributions of each feature from the perspective of statistics based on big naturalistic driving data. The aim is to comprehensively figure out different types of features in LC maneuvers and select the most contributive features over various LC scenarios.
VL  - 1
IS  - 3
SN  - 2399-9802
DO  - 10.1108/JICV-09-2018-0010
UR  - https://doi.org/10.1108/JICV-09-2018-0010
AU  - Li Xiaohan
AU  - Wang Wenshuo
AU  - Zhang Zhang
AU  - Rötting Matthias
PY  - 2018
Y1  - 2018/01/01
TI  - Effects of feature selection on lane-change maneuver recognition: an analysis of naturalistic driving data
T2  - Journal of Intelligent and Connected Vehicles
PB  - Emerald Publishing Limited
SP  - 85
EP  - 98
Y2  - 2024/04/23
ER  -