Rethinking real-time risk prediction from multi-step time series forecasting on highway car-following scenarios.

Driving risk prediction emerges as a pivotal technology within the driving safety domain, facilitating the formulation of targeted driving intervention strategies to enhance driving safety. The driving safety undergoes continuous evolution in response to the complexities of the traffic environment, representing a dynamic and ongoing serialization process. The evolutionary trend of this sequence offers valuable information pertinent to driving safety research. However, existing research on driving risk prediction has primarily concentrated on forecasting a single index, such as the driving safety level or the extreme value within a specified future timeframe. This approach often neglects the intrinsic properties that characterize the temporal evolution of driving safety. Leveraging the high-D natural driving dataset, this study employs the multi-step time series forecasting methodology to predict the risk evolution sequence throughout the car-following process, elucidates the benefits of the multi-step time series forecasting approach, and contrasts the predictive efficacy on driving safety levels across various temporal windows. The empirical findings demonstrate that the time series prediction model proficiently captures essential dynamics such as risk evolution trends, amplitudes, and turning points. Consequently, it provides predictions that are significantly more robust and comprehensive than those obtained from a single risk index. The TsLeNet proposed in this study integrates a 2D convolutional network architecture with a dual attention mechanism, adeptly capturing and synthesizing multiple features across time steps. This integration significantly enhances the prediction precision at each temporal interval. Comparative analyses with other mainstream models reveal that TsLeNet achieves the best performance in terms of prediction accuracy and efficiency. Concurrently, this research undertakes a comprehensive analysis of the temporal distribution of errors, the impact pattern of features on risk sequence, and the applicability of interaction features among surrounding vehicles. The adoption of multi-step time series forecasting approach not only offers a novel perspective for analyzing and exploring driving safety, but also furnishes the design and development of targeted driving intervention systems.

Overtaking maneuvers on two-lane highways under the microscope: Exploration of a multidimensional framework for the analysis of safety, comfort and efficiency using simulator data.

An unreasonable overtaking attempt on two-lane highways could cause drivers to suffer in terms of driving safety, comfort, and efficiency. Several external factors related to the traffic environment (e.g., speed and car type of surrounding vehicles), were found to be the significant factors in drivers' overtaking performance in the previous studies. However, the microscopic decision-making (e.g., the moments of the occupation of the opposite lane) mechanisms during overtaking, by means of which drivers react to changes in the external traffic environment and adjust their overtaking trajectories, are still need to be explored. Hence, this study had three goals: (i) To explore the spatial characteristics of micro-decisions (MDs) (such as the start and end point) in overtaking trajectories; (ii) To measure three types of performance indicators (i.e., safety, comfort, and efficiency) for the execution of overtaking maneuvers; (iii) To quantitatively explain the microscopic decision-making mechanism in overtaking. Data for overtaking trajectories were collected from driving a simulation experiment where 52 Chinese student drivers completed a series of overtaking maneuvers on a typical two-lane highway under different traffic conditions. Two analyses were conducted: firstly, the distributions of the relative distance between the ego and surrounding vehicles at four key points (i.e., the start, entry, back, and end) in the overtaking trajectory were investigated and clustered to uncover the spatial characteristics of the MDs. Secondly, the safety, comfort, and efficiency of the overtaking were measured by the aggregations of multi-targets collision risks, triaxial acceleration variances, and spatial consumptions respectively based on the Data Envelopment Analysis (DEA), which were further applied in a two-stage SEM model to reveal the quantitative interrelationships among the external factors, microscope decisions and performances in overtaking. We confirmed that the MDs could be considered as the mediating variables between the external factors and overtaking performances. In the presence of the more hazardous traffic environment (e.g., faster traffic flow and impeded by a truck), the safety, comfort and efficiency of overtaking would be deteriorated inevitably. But drivers would execute the overtaking under the longer passing sight distance, migrate their trajectories forward, and shorten the spatial duration to significantly improve the overtaking performances. Based on this mechanism, a overtaking trajectory optimization strategy for the advanced or automatic driving system, was confirmed and concluded that 1) the passing gap should be firstly planned according to the sight distance acceptance of different drivers, which directly determine the upper limit of the safety performance in the overtaking; 2) the trajectory forward migration and shortening the whole duration in overtaking could be effective to enhance the overtaking performances of the overtaking on the two-lane highway; 3) the guidance of the stable control of the steering wheel and gas/brake pedals is essential in the overtaking.