As the rapid development of automotive telematics,modern vehicles are expected to be connected through heterogeneous radio access technologies and are able to exchange massive information with their surrounding enviro...As the rapid development of automotive telematics,modern vehicles are expected to be connected through heterogeneous radio access technologies and are able to exchange massive information with their surrounding environment. By significantly expanding the network scale and conducting both real-time and long-term information processing, the traditional Vehicular AdHoc Networks(VANETs) are evolving to the Internet of Vehicles(Io V), which promises efficient and intelligent prospect for the future transportation system. On the other hand, vehicles are not only consuming but also generating a huge amount and enormous types of data, which is referred to as Big Data. In this article, we first investigate the relationship between Io V and big data in vehicular environment, mainly on how Io V supports the transmission, storage, computing of the big data, and how Io V benefits from big data in terms of Io V characterization,performance evaluation and big data assisted communication protocol design. We then investigate the application of Io V big data in autonomous vehicles. Finally, the emerging issues of the big data enabled Io V are discussed.展开更多
危险驾驶行为分析与预测是智能交通领域中的研究热点,但现有的驾驶行为分析模型要素单一、环境适应性不足,不能客观评估危险驾驶风险程度。为了解决上述问题,提出一种面向车联网(Internet of Vehicles,IoV)的自适应危险驾驶行为分析与...危险驾驶行为分析与预测是智能交通领域中的研究热点,但现有的驾驶行为分析模型要素单一、环境适应性不足,不能客观评估危险驾驶风险程度。为了解决上述问题,提出一种面向车联网(Internet of Vehicles,IoV)的自适应危险驾驶行为分析与预测框架。对驾驶行为数据进行深度分析,总结危险驾驶的影响因素;提出一种新型基于模糊逻辑的危险驾驶风险评估算法,从宏观角度评估驾驶员当前危险驾驶的风险等级;提出一种基于DB-LSTM(Driving Behavior based Long-Short Term Memory)的危险驾驶风险预测算法,用于预测驾驶员下一阶段的危险驾驶程度;在DDD17真实IoV数据集上进行大量对比试验。实验结果表明,提出模型的性能优于其他主流方法,具有良好的准确性和可用性。展开更多
基金supported by the National Natural Science Foundation of China(91638204)Natural Sciences and Engineering Research Council(NSERC)
文摘As the rapid development of automotive telematics,modern vehicles are expected to be connected through heterogeneous radio access technologies and are able to exchange massive information with their surrounding environment. By significantly expanding the network scale and conducting both real-time and long-term information processing, the traditional Vehicular AdHoc Networks(VANETs) are evolving to the Internet of Vehicles(Io V), which promises efficient and intelligent prospect for the future transportation system. On the other hand, vehicles are not only consuming but also generating a huge amount and enormous types of data, which is referred to as Big Data. In this article, we first investigate the relationship between Io V and big data in vehicular environment, mainly on how Io V supports the transmission, storage, computing of the big data, and how Io V benefits from big data in terms of Io V characterization,performance evaluation and big data assisted communication protocol design. We then investigate the application of Io V big data in autonomous vehicles. Finally, the emerging issues of the big data enabled Io V are discussed.
文摘危险驾驶行为分析与预测是智能交通领域中的研究热点,但现有的驾驶行为分析模型要素单一、环境适应性不足,不能客观评估危险驾驶风险程度。为了解决上述问题,提出一种面向车联网(Internet of Vehicles,IoV)的自适应危险驾驶行为分析与预测框架。对驾驶行为数据进行深度分析,总结危险驾驶的影响因素;提出一种新型基于模糊逻辑的危险驾驶风险评估算法,从宏观角度评估驾驶员当前危险驾驶的风险等级;提出一种基于DB-LSTM(Driving Behavior based Long-Short Term Memory)的危险驾驶风险预测算法,用于预测驾驶员下一阶段的危险驾驶程度;在DDD17真实IoV数据集上进行大量对比试验。实验结果表明,提出模型的性能优于其他主流方法,具有良好的准确性和可用性。
