基于现场数据的自动驾驶接驳车探索性驾驶性能与跟驰建模
Exploratory Driving Performance and Car-Following Modeling for Autonomous Shuttles Based on Field Data
AI总结：
本研究基于超过4000秒的自动驾驶接驳车现场跟驰数据，分析其驾驶性能并评估多个跟驰模型，发现标定的ACC模型误差最低，且自动驾驶接驳车具有较低峰值加速度和较高减速度，为运营评估与乘客舒适度改进提供依据。
摘要：
自动驾驶接驳车（AS）已在多个城市运营，并展现出改善公共交通网络的潜力。然而，目前尚缺乏基于现场数据的跟驰模型，使决策者能够评估和规划自动驾驶接驳车的运营。为填补这一空白，本研究收集了自动驾驶接驳车的现场数据，分析了其驾驶性能，并建议对自动驾驶接驳车轨迹模型进行修改以提升乘客舒适度。样本采集了自动驾驶接驳车跟随传统汽车超过4000秒的数据。样本包含自动驾驶接驳车和传统车辆的GPS位置。纬度和经度位置被用于计算前车与跟随车的速度、加速度和加加速度。数据分析表明，自动驾驶接驳车具有较高的加加速度值，可能影响乘客舒适度。研究人员评估了多个现有模型，并得出结论：经过标定的ACC模型在自动驾驶接驳车的间距和速度方面产生了更低的误差。标定结果表明，与其他研究中为自动驾驶车辆模型标定的参数相比，该自动驾驶接驳车具有较低的峰值加速度和较高的减速度。
英文摘要：
Autonomous shuttles (AS) operate in several cities and have shown potential to improve the public transport network. However, there is no car following model that is based on field data and allows decision-makers to assess and plan for AS operations. To fill this gap, this study collected field data from AS, analyzed their driving performance, and suggested changes in the AS trajectory model to improve passenger comfort. A sample was collected with more than 4000 seconds of AS following a conventional car. The sample contained GPS positions from both AS and conventional vehicles. Latitude and longitude positions were used to calculate the speed, acceleration, and jerk of the leader and follower. The data analyses indicated that AS have higher jerk values that may impact the passengers comfort. Several existing models were evaluated, and the researchers concluded that the calibrated ACC model resulted in lower errors for AS spacing and speed. The results of the calibration indicate that the AS has lower peak acceleration and higher deceleration than the parameters that were calibrated for autonomous vehicle models in other research
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
