Driving prediction techniques (DPTs) are used to forecast the distributions of various future driving conditions (FDC), like velocity, acceleration, driver behaviors etc. and the quality of prediction results has great impacts on the performance of corresponding predictive energy management strategies (PEMSs), e.g., fuel economy (FE), lifetime of battery etc. This survey presents a comprehensive study on existing DPTs. Firstly, a review on prediction objectives and major types of prediction algorithms are presented. Then a comparative study on various prediction approaches is carried out and suitable application scenarios for each approach are provided according to their characteristics. Moreover, prediction accuracy-affecting factors are analyzed and corresponding approaches for dealing with mis-predictions are discussed in detail. Finally, the bottlenecks of current researches and future developing trends of DPTs are given. In general, this paper not only gives a comprehensive analysis and review of existing DPTs but also indicates suitable application scenarios for each prediction algorithm and summarizes potential approaches for handling the prediction inaccuracies, which will help prospective designers to select proper DPTs according to different applications and contribute to the further performance enhancements of PEMSs for hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs).

驾驶预测技术（DPT）用于预测各种未来驾驶条件（FDC）的分布，例如速度，加速度，驾驶员行为等，并且预测结果的质量对相应的预测能源管理策略（PEMS）的性能有很大影响），例如燃油经济性（FE），电池寿命等。该调查对现有DPT进行了全面的研究。首先，对预测目标和预测算法的主要类型进行了综述。然后对各种预测方法进行了比较研究，并根据它们的特点提供了每种方法的合适应用场景。此外，分析了影响预测准确性的因素，并详细讨论了处理错误预测的相应方法。最后，给出了DPTs目前研究的瓶颈和未来的发展趋势。总的来说，本文不仅对现有的DPT进行了全面的分析和综述，还指出了每种预测算法的适用应用场景，并总结了处理预测不准确的潜在方法，这将有助于准设计师根据不同的应用选择合适的DPT。有助于进一步提高混合动力汽车（HEV）和插电式混合动力汽车（PHEV）的PEMS的性能。