车路协同信息融合的智能汽车行驶状态模糊评判

为了监测与评判道路上行驶的智能汽车的实时状态,基于研发的智能汽车的车载感知系统,包括通过视觉传感器、激光雷达、GPS定位、车载传感器系统及车载总线获取车内及周围环境信息。采用V2X通讯设备等获取路侧端雷视一体机、路侧传感器、气象传感器传输的交通信息,通过V2X通讯设备、4G通讯模块传送到云服务器并建立模糊评判模型。基于可信度的模糊推理算法对环境信息和交通信息进行融合,并以此为依据对行驶车辆的状态进行评判。首先,建立针对车辆行驶状态的模糊评判集合和各参数隶属度函数,计算出各参数的隶属度,并对行驶车辆的各个参数建立典型的行驶状态评判参数数据集合。其次,采用模糊假言推理方法,以典型的数据集合为基础建立带可信度和阈值的模糊规则库。应用麦姆德尼方法,建立与规则库的每个规则所对应的模糊关系矩阵库。以车辆行驶时接收到的车载端和路侧端信息作为输入,应用规则库规则进行带有可信度的模糊推理。然后,以相似度作为匹配度,对推理规则设定阈限,按照证据与规则的前件不相等的情况,计算结论的可信度得出结论。对结论进行冲突消解时,冲突消解的策略为取可信度高的结论。最后,应用匹配度对结论的可靠性进行验证,并在多个道路场景实时行驶的车辆上对算法进行试验验证。研究结果表明:算法对行驶车辆状态的评判与实车的状态相一致,可实现对车辆不安全状态的报警与行驶状态的干预,对保障行车安全有显著积极的实际应用意义。

Fuzzy Evaluation of an Intelligent-vehicle Driving State Based on a Vehicle-road Collaborative Information Fusion

This study monitored and judged the real-time state of intelligent vehicles running on the road, based on the vehicle's developed onboard sensing system, which obtains information on the vehicle's interior and surrounding environment through a visual sensor, LiDAR, GPS positioning, an onboard sensor system, and a vehicular bus. Vehicle-to-everything (V2X) communication equipment was applied to obtain the traffic information transmitted by a ray-vision integrated machine for roadside, roadside sensors, and meteorological sensors, which was transmitted to a cloud server through V2X communication equipment and a 4G communication module, and used to establish a fuzzy evaluation model. A fuzzy reasoning algorithm, based on credibility, was applied to fuse the environmental and traffic information, and the state of the running vehicles was evaluated. First, the fuzzy evaluation set and parameter membership function for the vehicle driving state were established, the membership degree of each parameter was calculated, and a typical driving-state evaluation parameter dataset was established for each parameter of the vehicle. Second, a fuzzy hypothetical-reasoning method was applied to establish a fuzzy rule database with credibility and threshold based on typical datasets. A fuzzy relation matrix library, corresponding to each rule of the rule database, was established using the Mamdani method. Using the information from the vehicle device and the roadside unit as input, fuzzy reasoning with credibility was conducted using the rules of the rule database. Then, taking the similarity as the matching degree, a threshold was set for the reasoning rule, and the reliability of the conclusion was calculated, considering that the evidence is not equal to the antecedent of the rule. When resolving the conflict of conclusions, the conflict-resolution strategy was to select a conclusion with high reliability. Finally, the reliability of the conclusion was verified using the matching degree, and the algorithm was verified by experiments on real-time vehicles in multiple road scenes. The experimental data analysis shows that the evaluation of the running vehicle state by the algorithm is consistent with the real vehicle state. Moreover, it can sound an alarm for an unsafe vehicle state and intervene in a running state, which has significant practical application to ensure driving safety.