主动安全出行预警数据

该数据使用在车载系统或导航APP中，提供实时预警服务。通过车道偏离，盲区监测等预警，直接预防交通事故。通过疲劳、分心预警，辅助驾驶员保持良好状态，降低驾驶疲劳。以司机ID为维度，检测到双手离开警告和视线离开路面时间过长时，可触发最高级别的预警，通过强烈警示强制驾驶员接管车辆，避免事故。1、数据采集：通过公司车载传感器，全方位，实时的采集驾驶环境与状态信号数据，包括司机ID、驾驶员反应、偏离次数、纠正时间、盲区车辆检测记录、注意力分散、疲劳程度。
2、数据处理：对数据进行清洗、融合与特征提取，剔除传感器短时失效产生的极不合理数据，已对司机ID匿名化处理，无法通过技术手段还原出原始数据。
3、本数据产品采用的“多传感器融合算法（Multi-sensor Fusion）”，综合摄像头、雷达的感知结果，得出对周围环境更可靠、更精确的理解，减少单一传感器的误判。
预警等级是一个基于阈值与规则来判定的。1、疲劳程度“高”，偏离次数 > 3，危险距离低于1米，属于高度警告；2、视线离开路面时间 > 3.0，疲劳程度“中等”，偏离次数2-3次(5分钟内)，属于中度警告等；3、视线离开路面时间 >= 2.0 且 < 3.0秒，偏离次数 = 1 或 2，注意力分散 "高"，属于低度警告；4、当以上所有条件均不满足时，系统状态即为“无警告”。

This dataset is deployed in in-vehicle systems or navigation applications to provide real-time warning services. It directly prevents traffic accidents via warnings such as lane departure alerts and blind spot monitoring. It also helps drivers maintain good driving conditions and alleviate driving fatigue through fatigue and distraction warnings. For each driver identified by their unique driver ID, when prolonged hands-off-the-wheel and eyes-off-the-road behaviors are detected, the top-level warning can be triggered, which forces the driver to take over the vehicle via strong alerts to avoid traffic accidents.

1. Data Collection: The dataset collects comprehensive, real-time signals of driving environment and status via the company's in-vehicle sensors, including driver ID, driver's reaction, number of lane departure incidents, correction time, blind spot vehicle detection records, attention distraction level, and fatigue level.

2. Data Processing: The data undergoes cleaning, fusion and feature extraction. Extremely unreasonable data caused by short-term sensor failures is removed. Driver IDs have been fully anonymized, and the original personal data cannot be restored through any technical means.

3. The "Multi-sensor Fusion" algorithm adopted by this dataset product integrates the perception results of cameras and radars to achieve a more reliable and accurate understanding of the surrounding environment, reducing misjudgments from single sensors.

Warning levels are determined based on thresholds and rules:
1. High-level warning applies when fatigue level is "high", number of lane departure incidents > 3, and dangerous distance < 1 meter;
2. Medium-level warning applies when eyes off the road time > 3.0 seconds, fatigue level is "medium", and number of lane departure incidents is 2 to 3 times within 5 minutes;
3. Low-level warning applies when eyes off the road time >= 2.0 seconds and < 3.0 seconds, number of lane departure incidents is 1 or 2, and attention distraction level is "high";
4. When none of the above conditions are met, the system status is "No Warning".

搜集汇总

背景与挑战

背景概述

该数据集为主动安全出行预警数据，包含828条记录，每日更新，涵盖预警时间、危险距离、驾驶员反应等13个字段，用于车载系统实时预警以预防交通事故。数据通过多传感器融合算法处理，基于阈值规则判定预警等级，支持疲劳、分心等驾驶状态监测。