Optimising driver profiling through behaviour modelling of in-car sensor and global positioning system data
Optimising driver profiling through behaviour modelling of in-car sensor and global positioning system data
复制标题
通过车内传感器和全球定位系统数据的行为建模优化驾驶员分析
DOI：
10.1016/j.compeleceng.2021.107047
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发表时间：
2021
影响因子：
4.3
通讯作者：
Ahmadi-Assalemi G
中科院分区：
文献类型：
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作者：
Ahmadi-Assalemi G
联网汽车对汽车行业产生了巨大的影响，虽然这种催化剂和颠覆性技术带来了威胁，但它也带来了解决现有车辆相关犯罪(如劫车)的机会。联网汽车配备了传感器，并能够进行复杂的计算处理，作为分层安全的手段，可以用来对司机进行建模和区分。我们生成了一个数据集，收集了伦敦市14小时的驾驶数据。这条路线长8.1英里，包括各种路况，如环形交叉路口、红绿灯和几个速度区。我们对驾驶片段中的特征进行识别和排序，使用随机森林对样本进行分类，并在S驾驶窗口大小为10的情况下，以98.84%的准确率(95%的置信度)优化了基于学习的模型。发现了驾驶模式的差异，以区分女性和男性司机，特别是通过纵向加速度、驾驶速度、扭矩和每分钟转数的差异来区分。
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.