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Recognizing speed limit information is crucial for advanced driver assistance systems (ADAS) as it directly affects the safety planning and decision-making process of intelligent driving systems. However, traditional image recognition-based solutions confront inherent restrictions and precision issues due to uncontrolled external factors. This paper endeavors to present a novel, data-driven solution for speed limit information recognition that leverages the stability and maturity of data-driven technologies, overcoming these challenges. We introduce Pareto-GBDTMO, a cutting-edge method that synergistically blends Gradient Boosting Decision Trees for Multiple Output (GBDT-MO) and Fast Pareto Feature Selection (FPFS). This integration is instrumental in discerning salient features to direct and expedite the learning process of GBDT-MO. When coupled with Bayesian optimization, the feature set undergoes dynamic updates at each boosting iteration, allowing GBDT-MO to concentrate on the most prominent features. This adaptive, relevance-guided feature space regularization mechanism enhances the efficiency and precision of speed limit recognition. Fujian Province highway electronic toll collection (ETC) data is used for further validation, and the experimental results emphasize the effectiveness of our model, with an high accuracy of 97%, a low loss rate of 0.7%, and minimal latency. These findings affirm the feasibility and scientific validity of our data-driven approach, offering a reliable and redundant solution for speed limit information recognition in ADAS. This study not only contributes to the practical application of ADAS but also lays the groundwork for future large-scale lane-level data research

Recognizing speed limit information is crucial for advanced driver assistance systems (ADAS) as it directly affects the safety planning and decision-making process of intelligent driving systems. However, traditional image recognition-based solutions confront inherent restrictions and precision issues due to uncontrolled external factors. This paper endeavors to present a novel, data-driven solution for speed limit information recognition that leverages the stability and maturity of data-driven technologies, overcoming these challenges. We introduce Pareto-GBDTMO, a cutting-edge method that synergistically blends Gradient Boosting Decision Trees for Multiple Output (GBDT-MO) and Fast Pareto Feature Selection (FPFS). This integration is instrumental in discerning salient features to direct and expedite the learning process of GBDT-MO. When coupled with Bayesian optimization, the feature set undergoes dynamic updates at each boosting iteration, allowing GBDT-MO to concentrate on the most prominent features. This adaptive, relevance-guided feature space regularization mechanism enhances the efficiency and precision of speed limit recognition. Fujian Province highway electronic toll collection (ETC) data is used for further validation, and the experimental results emphasize the effectiveness of our model, with an high accuracy of 97%, a low loss rate of 0.7%, and minimal latency. These findings affirm the feasibility and scientific validity of our data-driven approach, offering a reliable and redundant solution for speed limit information recognition in ADAS. This study not only contributes to the practical application of ADAS but also lays the groundwork for future large-scale lane-level data research [ABSTRACT FROM AUTHOR]