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为实现人机共驾模式下智能系统对驾驶人换道决策的准确识别,将换道决策细分并提出了基于改进的极端梯度提升(XGBoost)的换道决策识别模型。以实车试验采集的自然驾驶数据作为输入,并采用滑动时间窗法确定识别时刻,建立各识别时间窗口下基于XGBoost的换道决策识别模型,同时运用交叉检验和网格搜索(GS)算法进一步提升模型性能,最后利用验证集数据评估所构建GS-XGBoost模型的识别性能,并与机器学习及深度学习模型进行对比。结果表明,所提出的模型在具体换道决策辨识上具有较好的实时性和准确性,且在1.8 s和1.6 s时间窗下的识别准确率最高,达到86.2%。 相似文献
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Vehicle-to-Vehicle communications provide the opportunity to create an internet of cars through the recent advances in communication technologies, processing power, and sensing technologies. A connected vehicle receives real-time information from surrounding vehicles; such information can improve drivers’ awareness about their surrounding traffic condition and lead to safer and more efficient driving maneuvers. Lane-changing behavior, as one of the most challenging driving maneuvers to understand and to predict, and a major source of congestion and collisions, can benefit from this additional information. This paper presents a lane-changing model based on a game-theoretical approach that endogenously accounts for the flow of information in a connected vehicular environment. A calibration approach based on the method of simulated moments is presented and a simplified version of the proposed framework is calibrated against NGSIM data. The prediction capability of the simplified model is validated. It is concluded the presented framework is capable of predicting lane-changing behavior with limitations that still need to be addressed. Finally, a simulation framework based on the fictitious play is proposed. The simulation results revealed that the presented lane-changing model provides a greater level of realism than a basic gap-acceptance model. 相似文献
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Zuduo Zheng Soyoung Ahn Danjue ChenJorge Laval 《Transportation Research Part B: Methodological》2011,45(9):1378-1388
In this paper we identify the origins of stop-and-go (or slow-and-go) driving and measure microscopic features of their propagations by analyzing vehicle trajectories via Wavelet Transform. Based on 53 oscillation cases analyzed, we find that oscillations can be originated by either lane-changing maneuvers (LCMs) or car-following (CF) behavior. LCMs were predominantly responsible for oscillation formations in the absence of considerable horizontal or vertical curves, whereas oscillations formed spontaneously near roadside work on an uphill segment. Regardless of the trigger, the features of oscillation propagations were similar in terms of propagation speed, oscillation duration, and amplitude. All observed cases initially exhibited a precursor phase, in which slow-and-go motions were localized. Some of them eventually transitioned into a well-developed phase, in which oscillations propagated upstream in queue. LCMs were primarily responsible for the transition, although some transitions occurred without LCMs. Our findings also suggest that an oscillation has a regressive effect on car-following behavior: a deceleration wave of an oscillation affects a timid driver (characterized by larger response time and/or minimum spacing) to become less timid and an aggressive driver less aggressive, although this change may be short-lived. An extended framework of Newell’s CF model is able to describe the regressive effect with two additional parameters with reasonable accuracy, as verified using vehicle trajectory data. 相似文献
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This paper focuses on the lane-changing trajectory planning (LTP) process in the automatic driving technologies. Existing studies on the LTP algorithms are primarily the static planning method in which the states of the surrounding vehicles of a lane-changing vehicle are assumed to keep unchanged in the whole lane-changing process. However, in real-world traffic, the velocities of the surrounding vehicles change dynamically, and the lane-changing vehicle needs to adjust its velocity and positions correspondingly in real-time to maintain safety. To address such limitations, the dynamic lane-changing trajectory planning (DLTP) model is proposed in the limited literature. This paper proposes a novel DLTP model consisting of the lane-changing starting-point determination module, trajectory decision module and trajectory generation module. The model adopts a time-independent polynomial trajectory curve to avoid the unrealistic assumptions on lane-changing velocities and accelerations in the existing DLTP model. Moreover, a rollover-avoidance algorithm and a collision-avoidance algorithm containing a reaction time are presented to guarantee the lane-changing safety of automated vehicles, even in an emergent braking situation. The field lane-changing data from NGSIM data are used to construct a real traffic environment for lane-changing vehicles and verify the effectiveness of the proposed model, and CarSim is applied to investigate the traceability of the planned lane-changing trajectories using the proposed model. The results indicate that an automated vehicle can complete the lane-changing process smoothly, efficiently and safely following the trajectory planned by the proposed model, and the planned velocity and trajectory can be well-tracked by automated vehicles. 相似文献
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跟驰和换道是交通流理论重要的研究方向,换道行为涉及因素较跟驰行为更为复杂。当前基于国外公开轨迹数据集的换道特性分析很难涵盖符合中国驾驶人特性的换道行为特性,同时国内外数据集采集来源多集中在高速公路上,未考虑不同道路类型对换道行为特性的影响。为研究中国典型城市道路车辆换道行为特性,采用无人机对武汉城市快速路直行路段交通流进行拍摄,获取符合中国城市道路特性与驾驶人特性的自然驾驶数据,并对数据集进行换道识别与参数提取,在此基础上进行了换道行为特性分析。无人机所采集视频包含小型车辆8 609辆,依据车辆所在车道编号是否发生变化以及变化次数作为换道车辆识别标准,共提取6 897辆跟驰车辆轨迹数据（车辆所在车道编号无变化）以及1 712辆单次换道车辆轨迹数据（车辆所在车道编号仅发生一次变化）。基于所提取跟驰车辆轨迹数据获取道路交通流平均速度与车辆平均跟车间距等指标,从而对交通流实时运行状态进行分析;基于所提取的车辆单次换道轨迹数据,采用固定时间窗口作为判断换道起终点的依据,在此基础上获取车辆换道纵向位移与换道启动时与周边车的时距,并结合交通流实时运行状态进行换道行为安全分析。通过对所获取的跟驰与换道交通特征参数进行分布拟合与统计分析,结果显示道路交通流速度均值为19.257 1 m/s,车辆跟车间距均值为45.910 7 m,车辆换道纵向位移均值为115.515 m,车辆换道启动时与周边车时距分布均符合对数正态分布。其中换道车辆与目标车道前车时距均值显著高于初始车道前车时距均值。同时发现,在与目标车道后车时距较小时,仍有一部分驾驶人选择换道,这体现了部分驾驶人激进的驾驶行驶。本研究可为分析中国城市快速路上的换道特性以及开发适用于中国交通特点的换道行为模型提供参考。 相似文献
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This paper shows that the behavior of driver models, either individually or entangled in stochastic traffic simulation, is affected by the accuracy of empirical vehicle trajectories. To this aim, a “traffic-informed” methodology is proposed to restore physical and platoon integrity of trajectories in a finite time–space domain, and it is applied to one NGSIM I80 dataset. However, as the actual trajectories are unknown, it is not possible to verify directly whether the reconstructed trajectories are really “nearer” to the actual unknowns than the original measurements. Therefore, a simulation-based validation framework is proposed, that is also able to verify indirectly the efficacy of the reconstruction methodology. The framework exploits the main feature of NGSIM-like data that is the concurrent view of individual driving behaviors and emerging macroscopic traffic patterns. It allows showing that, at the scale of individual models, the accuracy of trajectories affects the distribution and the correlation structure of lane-changing model parameters (i.e. drivers heterogeneity), while it has very little impact on car-following calibration. At the scale of traffic simulation, when models interact in trace-driven simulation of the I80 scenario (multi-lane heterogeneous traffic), their ability to reproduce the observed macroscopic congested patterns is sensibly higher when model parameters from reconstructed trajectories are applied. These results are mainly due to lane changing, and are also the sought indirect validation of the proposed data reconstruction methodology. 相似文献
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为解决智能车辆在传统环境及联网环境中换道行为的决策问题,文章基于博弈论建立智能车辆的换道行为模型,并使用双层规划算法对该模型进行分析。使用 HighD 数据库和驾驶模拟器采集的数据对模型进行了仿真分析。结果表明,该模型可以预测在联网环境和传统环境下车辆换道行为中各参与者的最优策略,模型的预测结果可以为智能车辆的换道策略选择提供指导,为智能交通系统的设计和实施提供重要参考,有助于提高交通效率和驾驶安全性。 相似文献
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