用机器学习分析车辆安全数据，预测5星评级可能性。
Predicting NCAP Safety Ratings: An Analysis of Vehicle Characteristics and ADAS Features Using Machine Learning
- 基于4种机器学习模型，融合车辆参数与ADAS功能预测安全评级。
- 优化后的随机森林模型准确率达89.18%，AUC达0.9586。
- 发现车重和年份最重要，但ADAS功能仍具显著预测价值。
车辆安全评估对消费者决策和监管至关重要。新车型评估计划（NCAP）提供标准化安全评分，传统侧重被动安全，现也纳入高级驾驶辅助系统（ADAS）等主动安全技术。本研究探讨特定ADAS功能（如前向碰撞预警、车道偏离警告、紧急制动预警、盲点监测）及传统车辆属性（如整备质量、车型年份、车辆类型、驱动形式）是否能可靠预测车辆获得最高（5星）综合评级的可能性。基于涵盖2011-2025年约5,128种车型变体的公开NCAP数据集，比较了逻辑回归、随机森林、梯度提升和支持向量分类器（SVC）四种模型，采用5折分层交叉验证。表现最佳的随机森林与梯度提升模型经随机搜索超参数优化。特征重要性分析显示，整备质量和车型年份在随机森林模型中贡献超过55%的特征相关性，主导预测能力。然而，引入ADAS功能仍带来有意义的预测增益。优化后的随机森林模型在保留测试集上表现稳健，准确率为89.18%，ROC AUC为0.9586。研究揭示了机器学习在大规模NCAP数据分析中的应用潜力，凸显了传统车辆参数与现代ADAS功能在实现顶级安全评级中的协同预测作用。
原文摘要 · Abstract (English)
Vehicle safety assessment is crucial for consumer information and regulatory oversight. The New Car Assessment Program (NCAP) assigns standardized safety ratings, which traditionally emphasize passive safety measures but now include active safety technologies such as Advanced Driver-Assistance Systems (ADAS). It is crucial to understand how these various systems interact empirically. This study explores whether particular ADAS features like Forward Collision Warning, Lane Departure Warning, Crash Imminent Braking, and Blind Spot Detection, together with established vehicle attributes (e.g., Curb Weight, Model Year, Vehicle Type, Drive Train), can reliably predict a vehicle's likelihood of earning the highest (5-star) overall NCAP rating. Using a publicly available dataset derived from NCAP reports that contain approximately 5,128 vehicle variants spanning model years 2011-2025, we compared four different machine learning models: logistic regression, random forest, gradient boosting, and support vector classifier (SVC) using a 5-fold stratified cross-validation approach. The two best-performing algorithms (random forest and gradient boost) were hyperparameter optimized using RandomizedSearchCV. Analysis of feature importance showed that basic vehicle characteristics, specifically curb weight and model year, dominated predictive capability, contributing more than 55% of the feature relevance of the Random Forest model. However, the inclusion of ADAS features also provided meaningful predictive contributions. The optimized Random Forest model achieved robust results on a held-out test set, with an accuracy of 89.18% and a ROC AUC of 0.9586. This research reveals the use of machine learning to analyze large-scale NCAP data and highlights the combined predictive importance of both established vehicle parameters and modern ADAS features to achieve top safety ratings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。