Improving the Level of Responsibility Classification for Pedestrian Crashes with the Multilayer Perceptron Model

Pedestrian crashes cause the most injuries of all types of traffic crashes. Despite their direct judicial and societal impact, the automatic classification of legal responsibility remains largely unexplored. This work addresses this gap by formulating the responsibility assessment problem as a supervised multi-class classification task and proposing a Multilayer Perceptron (MLP) based decision-support system. The objective is to establish the basis for a “robot judge” application that assists the Judicial Traffic Police (JTP), Courts, and Prosecutors in identifying cases with a clear level of responsibility in pedestrian crashes. This study draws on real-world data from reports by the Local Police of Badajoz (LPB) and Spanish Judiciary (SJ) judicial decisions. After rigorous data preprocessing, 14 meaningful binary variables were identified. The level of responsibility in a pedestrian crash depends on these 14 variables, which constitute the feature space used to model responsibility as a five-category output variable. We were able to reclassify the categories of each pedestrian crash and improve the metrics using the MLP model. More precise levels of responsibility could be determined. This would help the JPT and the Courts make more efficient and objective final decisions in similar cases. It would also enable them to focus their efforts on more complex cases requiring further investigation by human specialists. In turn, policymakers could take new measures to reduce pedestrian crashes by analyzing influential variables.