电动汽车正面碰撞试验技术研究与分析

针对电动汽车结构特点和特性,总结和分析了国内外电动汽车正面碰撞试验相对应的电安全法规和标准,提出了电动汽车正面碰撞试验程序和评价方法。进行了电动汽车的实车正面碰撞试验,验证了试验程序和评价方法,比较和分析了原型车和电动汽车的碰撞试验结果,揭示电动汽车在正面碰撞形式下的碰撞特性,以及现行安全标准中存在的问题。     

基于驾驶风格的前撞预警系统报警策略

考虑驾驶风格的影响,设计了一种优化的纵向相对距离预测模型,并基于此模型改进了前撞预警系统报警策略.驾驶风格分类结合了分位点法和信息熵法,以不同方式进行特征提取,使用k-means方法聚类样本数据.基于长短期记忆模型,设计了编码器-解码器模型用于预测,以上述分类的全部数据训练模型的共用部分参数,以提高模型的泛化能力;而以...     

基于多任务迁移学习的交通警情信息自动处理方法

从交通警情数据中自动获取信息对于快速处理交通事故和提高交通管理水平具有重要的意义。为此，提出了一种基于多任务迁移学习的交通警情信息自动处理方法，该方法上游采用文本预训练模型作为共享参数层，下游建立多任务并行学习方法，实现对交通警情中的关键信息、类型和语义自动处理。选取江苏省苏州市2年内共120 191条原始交通警情作为试验数据，通过自动处理方法构建了一套标准的交通警情信息数据库。试验结果表明：所建立的关键信息抽取方法可以更精准地提取警情数据中的时间、地址和车牌信息；交通警情分类模型性能优于现有的深度学习模型，分类准确率达93%；基于局部特征增强的警情语义分析方法重点识别了警情中事故的严重程度和救援需求，识别准确率达87%。研究结论显示交通警情自动化处理方法具有良好的可移植性和实用性。     

A dynamic Bayesian network model for real-time crash prediction using traffic speed conditions data

Traffic crashes occurring on freeways/expressways are considered to relate closely to previous traffic conditions, which are time-varying. Meanwhile, most studies use volume/occupancy/speed parameters to predict the likelihood of crashes, which are invalid for roads where the traffic conditions are estimated using speed data extracted from sampled floating cars or smart phones. Therefore, a dynamic Bayesian network (DBN) model of time sequence traffic data has been proposed to investigate the relationship between crash occurrence and dynamic speed condition data. Moreover, the traffic conditions near the crash site were identified as several state combinations according to the level of congestion and included in the DBN model. Based on 551 crashes and corresponding speed information collected on expressways in Shanghai, China, DBN models were built with time series speed condition data and different state combinations. A comparative analysis of the DBN model using flow detector data and a static Bayesian network model was also conducted. The results show that, with only speed condition data and nine traffic state combinations, the DBN model can achieve a crash prediction accuracy of 76.4% with a false alarm rate of 23.7%. In addition, the results of transferability testing imply that the DBN models are applicable to other similar expressways with 67.0% crash prediction accuracy.     

船舶操纵性评估体系构架设计

针对船舶航行安全和操纵安全的需求,初步设计了船舶操纵性评估系统,给出了系统逻辑框架和评估指标体系。船舶操纵性评估模块是该船舶操纵性评估系统的核心,其评估指标体系由3个一级评估指标和11个二级评估指标组成,层次化特征明显。对部分二级评估指标给出了工程上可行的计算模型和评估准则。最后给出某货船的试验结果,验证了计算模型的实用性。     

正面碰撞中假人大腿的伤害

为了掌握假人大腿伤害机理,解决汽车实际碰撞中假人大腿伤害超标问题,文章首先通过应用Hyperworks和Dyna等软件仿真分析得出大腿伤害的机理,即碰撞中假人下肢与仪表板第1个接触点是假人的小腿且会造成较大的膝盖滑移量,然后针对具体车型问题提出降低仪表板刚度的优化方案,最终通过实车碰撞试验验证解决方案的有效性。提出保证碰撞中假人下肢与仪表板的第1个接触点是假人膝盖的IP型面的设计方案。     

Effects of crash warning systems on rear-end crash avoidance behavior under fog conditions

Reduced visibility conditions increase both the probability of rear-end crash occurrences and their severity. Crash warning systems that employ data from connected vehicles have potential to improve vehicle safety by assisting drivers to be aware of the imminent situations ahead in advance and then taking timely crash avoidance action(s). This study provides a driving simulator study to evaluate the effectiveness of the Head-up Display warning system and the audio warning system on drivers’ crash avoidance performance when the leading vehicle makes an emergency stop under fog conditions. Drivers’ throttle release time, brake transition time, perception response time, brake reaction time, minimum modified time-to-collision, and maximum brake pedal pressure are assessed for the analysis. According to the results, the crash warning system can help decrease drivers’ reaction time and reduce the probability of rear-end crashes. In addition, the effects of fog level and drivers’ characteristics including gender and age are also investigated in this study. The findings of this study are helpful to car manufacturers in designing rear-end crash warning systems that enhance the effectiveness of the system’s application under fog conditions.     

100%低地板城市有轨电车车辆碰撞仿真分析

为提高100%低地板列车在意外碰撞事故中的被动安全性能,依据欧洲标准EN15227《铁路车辆车体的防碰撞性规范》进行车辆的碰撞仿真设计和计算分析,得到了具有良好碰撞安全防护性能的车辆设计结构,为现代轨道车辆的耐碰撞设计提供了参考.     

某车型左纵梁变形吸能模式优化分析

某车型在进行偏置碰撞试验时,产生左纵梁变形区域偏小,变形吸能模式十分不理想;同时导致能量后传,前围变形过大,无法满足碰撞吸能要求。通过系统分析左纵梁可变形区域、刚度特性、抗弯特性,针对存在的问题,优化了此款车型左纵梁上附件的布置形式及可变形区域,并在原结构基础上调整纵梁结构变形特性。最后,通过实车验证试验对结果进行验证,结果表明:优化后的整车结构碰撞安全性能能够满足碰撞安全要求,能够为星级目标达标提供理想的结构基础。     

Dynamic model for automotive side impact crashes

A rigid body model to represent a side impact crash is constructed using five degrees-of-freedom (dof) for the vehicle and three dof for each occupant in the vehicle. Nonlinear stiffness and damping elements and the presence of physical gaps between several components make the model highly nonlinear. The model is validated using experimental crash test data from a National Highway Traffic Safety Administration (NHTSA) database. To simplify the parameter identification process and reduce the number of parameters to be identified at each stage, a two-step process is adopted in which the vehicle is first assumed to be unaffected by the presence of the occupants, and its model parameters are identified. Subsequently, the parameters in the occupant models are identified.

The active set method with a performance index that includes both the L₂ and L_∞ norms is used for parameter identification. A challenge is posed by the fact that the optimisation problem involved is non-convex. To overcome this challenge, a large set of random initial values of parameter estimates is generated and the optimisation method is applied with all these initial conditions. The values of parameters that provide the minimal performance index from the entire set of initial conditions are then chosen as the best parameter values. The optimal parameters values thus identified are shown to significantly improve the match between the model responses and the experimentally measured sensor signals from the NHTSA crash test.