Multitask Learning and Prediction of Baseline Driving Performance Measures

Driving performance measures (DPMs) are important indices for driving and personal safety in vehicle operation. The DPMs are collected under various controlled driving conditions to demonstrate different driving behaviors so that mitigating technology interventions can be studied and designed. However, significant costs are involved in the DPM acquisition, and there are a very limited number of controlled driving condition data. Thus, the modeling and prediction of the DPMs under unobserved driving conditions are critical, and many methods have been developed. However, existing literature in this area suffer a common limitation: The interactions among different DPMs are not fully considered (each DPM is modeled individually), although the existence of such interactions is widely reported. The researcher developed and reported a novel DPM modeling and prediction method, i.e., multi-output convolutional Gaussian process (MCGP), that incorporates the interactions among different DPMs. The method features the modeling flexibility for different DPMs and the interpretable modeling structure for integrating the DPM interactions. The method is compared with three benchmark methods on the DPM data set under four different settings, and the results demonstrate the superiorities of the method. The proposed method provides flexible and accurate predictions for DPMs at unobserved driving conditions, which can significantly reduce simulation costs and time.

驾驶性能指标（Driving Performance Measures，DPMs）是车辆运行过程中关乎驾驶安全与人员安全的重要指标。此类指标通常在多种受控驾驶工况下采集，用以表征不同的驾驶行为，从而为驾驶风险缓解干预技术的研究与设计提供支撑。然而，DPMs的采集需承担高昂成本，且受控驾驶工况下的可用数据集规模极为有限。因此，在未观测到的驾驶工况下对DPMs进行建模与预测具有关键意义，当前已有诸多相关方法被提出。不过，该领域现有研究普遍存在一项共性局限：尽管不同DPM间的交互效应已被广泛证实，但现有方法大多未充分考量此类关联，通常仅对单个DPM进行独立建模。为此，本研究提出并公开了一种新型DPM建模与预测方法——多输出卷积高斯过程（Multi-output Convolutional Gaussian Process，MCGP），该方法可有效融合不同DPM间的交互关系。该方法兼具适配各类DPM的建模灵活性与整合DPM交互关系的可解释建模架构。本研究在四种不同实验配置下，将该方法与三种基准方法在DPM数据集上开展对比实验，结果证实了所提方法的性能优越性。所提方法可在未观测到的驾驶工况下实现DPMs的灵活精准预测，能够显著降低仿真实验的成本与时间开销。