Connected cars have a massive impact on the automotive sector, and whilst this catalyst and disruptor technology introduce threats, it brings opportunities to address existing vehicle-related crimes such as carjacking. Connected cars are fitted with sensors, and capable of sophisticated computational processing which can be used to model and differentiate drivers as means of layered security. We generate a dataset collecting 14 h of driving in the city of London. The route was 8.1 miles long and included various road conditions such as roundabouts, traffic lights, and several speed zones. We identify and rank the features from the driving segments, classify our sample using Random Forest, and optimise the learning-based model with 98.84% accuracy (95% confidence) given a small 10 s driving window size. Differences in driving patterns were uncovered to distinguish between female and male drivers especially through variations in longitudinal acceleration, driving speed, torque and revolutions per minute.

互联汽车对汽车行业产生了巨大影响，尽管这种催化剂和破坏性技术带来了威胁，但它也带来了解决现有与汽车有关的犯罪（例如劫车）的机会。联网汽车装有传感器，并且能够进行复杂的计算处理，可用于对驾驶员进行建模和区分，以作为分层安全性的手段。我们生成了一个数据集，该数据集收集了伦敦市内14小时的驾车时间。该路线长8.1英里，包括各种路况，例如回旋处，交通信号灯和多个速度区。我们从行驶细分中识别特征并对其进行排名，使用随机森林对样本进行分类，并在较小的10 s行驶窗口大小下以98.84％的准确性（95％的置信度）优化基于学习的模型。