基于外部动态环境的汽车碰撞危险估计算法研究
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摘要
针对现有紧急情况下车辆的碰撞危险评估算法大多只考虑量测噪声干扰带来的不确定性,提出一种综合考虑路面动态环境不确定性和量测噪声干扰的汽车碰撞危险估计算法。首先,构建"路面状况-车速-最大减速度"模糊推理模型,即由路面状况和自车车速,经模糊推理智能算法快速获取车辆制动最大减速度;建立基于运动学的预测模型,考虑上述路面附着状况动态变化和传感器量测噪声带来的不确定性,采用蒙特卡洛法实时计算自车当前行驶环境下的碰撞概率。根据汽车动力学和道路有关参数预测车辆紧急制动和转向的轨迹,从而得到制动避撞与换道避撞的碰撞概率。以交叉路口和追尾工况为例,对比分析了不同路面情况下制动避撞和转向避撞的碰撞概率,从而为车辆选择合理的避撞方式。结果表明,所提出的危险估计算法与真实交通动态环境下的紧急避撞行为比较相符,具有良好的有效性和可行性。
In view of that the most existing vehicle collision risk estimation algorithms under emergency only consider the uncertainty caused by measurement noise interference, a vehicle collision risk estimation algorithm is proposed concurrently considering both the uncertainty in dynamic road environment and that caused by measurement noise interference. Firstly, the fuzzy inference model for "road condition-speed-maximum acceleration" is constructed, in which the maximum braking acceleration of vehicle is quickly obtained by using the intelligent algorithm of fuzzy inference based on road condition and host vehicle speed. A kinematics-based prediction model is also established, and with consideration of the dynamic changes of road adhesion condition and the uncertainty caused by measurement noise interference, Monte Carlo method is used to calculate the crash probability of host vehicle under current driving environment. The trajectories of vehicle emergency braking and steering are predicted according to vehicle dynamics and road-related parameters and hence the crash probabilities with collision avoidance by braking or lane change are obtained. Taking the intersection crossing and rear end collision conditions as examples, the crash probabilities with braking and steering collision avoidances under different road conditions are contrastively analyzed so as to choose a reasonable mode of vehicle collision avoidance. The results show that the proposed risk estimation algorithm is relatively consistent with the emergency collision avoidance maneuver in real dynamic traffic environment with good effectiveness and feasibility.
In view of that the most existing vehicle collision risk estimation algorithms under emergency only consider the uncertainty caused by measurement noise interference, a vehicle collision risk estimation algorithm is proposed concurrently considering both the uncertainty in dynamic road environment and that caused by measurement noise interference. Firstly, the fuzzy inference model for "road condition-speed-maximum acceleration" is constructed, in which the maximum braking acceleration of vehicle is quickly obtained by using the intelligent algorithm of fuzzy inference based on road condition and host vehicle speed. A kinematics-based prediction model is also established, and with consideration of the dynamic changes of road adhesion condition and the uncertainty caused by measurement noise interference, Monte Carlo method is used to calculate the crash probability of host vehicle under current driving environment. The trajectories of vehicle emergency braking and steering are predicted according to vehicle dynamics and road-related parameters and hence the crash probabilities with collision avoidance by braking or lane change are obtained. Taking the intersection crossing and rear end collision conditions as examples, the crash probabilities with braking and steering collision avoidances under different road conditions are contrastively analyzed so as to choose a reasonable mode of vehicle collision avoidance. The results show that the proposed risk estimation algorithm is relatively consistent with the emergency collision avoidance maneuver in real dynamic traffic environment with good effectiveness and feasibility.
引文
[1] EIDEHALL A. Multi-Target threat assessment for automotive applications[C]. The 14th International IEEE Conference on Intelligent Transportation Systems, Washington, DC, USA:2011:433-438.
[2] 吴斌,朱西产,沈剑平.基于自然驾驶数据的危险评估算法研究[J].汽车工程,2017,39(8):907-914.
[3] ALTHOFF M, STURSBERG O, BUSS M. Model-based probabilistic collision detection in autonomous driving[M]. IEEE Press,2009.
[4] BáLINT A. Mathematical analysis of braking in rear-end crash scenarios[C]. Energy Conversion Congress and Exposition (ECCE),2012 IEEE. IEEE,2013:2078-2085.
[5] HUANG Z, LIU X, SONG X, et al. Probabilistic risk assessment for pedestrian-vehicle collision considering uncertainties of pedestrian mobility[J]. Traffic Injury Prevention,2017,18(6).
[6] 吴浩然.基于驾驶意图与动态环境的车辆纵向碰撞预警研究[D].武汉:武汉理工大学,2013.
[7] STELLET J E, STRAUB F, SCHUMACHER J, et al. Estimating the process noise variance for vehicle motion models[C]. IEEE, International Conference on Intelligent Transportation Systems. IEEE,2015:1512-1519.
[8] 江庆坤.智能汽车避障危险评估和轨迹规划研究[D].长春:吉林大学,2016.
[9] SCHUBERT R, RICHTER E, WANIELIK G. Comparison and evaluation of advanced motion models for vehicle tracking[C]. International Conference on Information Fusion, IEEE,2008:1-6.
[10] STELLET J E. Statistical modelling of algorithms for signal processing in systems based on environment perception[D]. Karlsruher Institut für Technologie,2016.
[11] BR?NNSTR?M M, SANDBLOM F, HAMMARSTRAND L. A probabilistic framework for decision-making in collision avoidance systems[J]. IEEE Transactions on Intelligent Transportation Systems,2013,14(2):637-648.
[12] SLEDGE N H, MARSHEK K M. Comparison of ideal vehicle lane-change trajectories[C]. SAE Paper 971062.
[13] SCHREIER M. Bayesian environment representationprediction,and criticality assessment for driver assistance systems[J]. at-Automatisierungstechnik,2017,65(2):151-152.
[14] LI X R, JILKOV V P. Survey of maneuvering target tracking. Part I. dynamic models[J]. IEEE Transactions on Aerospace & Electronic Systems,2004,39(4):1333-1364.
[15] STELLET J E, SCHUMACHER J, BRANZ W, et al. Uncertainty propagation in criticality measures for driver assistance[C]. Intelligent Vehicles Symposium, IEEE,2015:1187-1194.
[2] 吴斌,朱西产,沈剑平.基于自然驾驶数据的危险评估算法研究[J].汽车工程,2017,39(8):907-914.
[3] ALTHOFF M, STURSBERG O, BUSS M. Model-based probabilistic collision detection in autonomous driving[M]. IEEE Press,2009.
[4] BáLINT A. Mathematical analysis of braking in rear-end crash scenarios[C]. Energy Conversion Congress and Exposition (ECCE),2012 IEEE. IEEE,2013:2078-2085.
[5] HUANG Z, LIU X, SONG X, et al. Probabilistic risk assessment for pedestrian-vehicle collision considering uncertainties of pedestrian mobility[J]. Traffic Injury Prevention,2017,18(6).
[6] 吴浩然.基于驾驶意图与动态环境的车辆纵向碰撞预警研究[D].武汉:武汉理工大学,2013.
[7] STELLET J E, STRAUB F, SCHUMACHER J, et al. Estimating the process noise variance for vehicle motion models[C]. IEEE, International Conference on Intelligent Transportation Systems. IEEE,2015:1512-1519.
[8] 江庆坤.智能汽车避障危险评估和轨迹规划研究[D].长春:吉林大学,2016.
[9] SCHUBERT R, RICHTER E, WANIELIK G. Comparison and evaluation of advanced motion models for vehicle tracking[C]. International Conference on Information Fusion, IEEE,2008:1-6.
[10] STELLET J E. Statistical modelling of algorithms for signal processing in systems based on environment perception[D]. Karlsruher Institut für Technologie,2016.
[11] BR?NNSTR?M M, SANDBLOM F, HAMMARSTRAND L. A probabilistic framework for decision-making in collision avoidance systems[J]. IEEE Transactions on Intelligent Transportation Systems,2013,14(2):637-648.
[12] SLEDGE N H, MARSHEK K M. Comparison of ideal vehicle lane-change trajectories[C]. SAE Paper 971062.
[13] SCHREIER M. Bayesian environment representationprediction,and criticality assessment for driver assistance systems[J]. at-Automatisierungstechnik,2017,65(2):151-152.
[14] LI X R, JILKOV V P. Survey of maneuvering target tracking. Part I. dynamic models[J]. IEEE Transactions on Aerospace & Electronic Systems,2004,39(4):1333-1364.
[15] STELLET J E, SCHUMACHER J, BRANZ W, et al. Uncertainty propagation in criticality measures for driver assistance[C]. Intelligent Vehicles Symposium, IEEE,2015:1187-1194.