Decision-Making in Driver-Automation Shared Control:A Review and Perspectives

Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driver’s abilities to control.The human driver,as an essential agent in the driver-vehicle shared control systems,should be precisely modeled regarding their cognitive processes,control strategies,and decision-making processes.The interactive strategy design between drivers and automated driving agents brings an excellent challenge for human-centric driver assistance systems due to the inherent characteristics of humans.Many open-ended questions arise,such as what proper role of human drivers should act in a shared control scheme?How to make an intelligent decision capable of balancing the benefits of agents in shared control systems?Due to the advent of these attentions and questions,it is desirable to present a survey on the decision making between human drivers and highly automated vehicles,to understand their architectures,human driver modeling,and interaction strategies under the driver-vehicle shared schemes.Finally,we give a further discussion on the key future challenges and opportunities.They are likely to shape new potential research directions.

Flood Generation Mechanisms and Potential Drivers of Flood in Wabi-Shebele River Basin, Ethiopia

Flood is a natural process generated by the interaction of various driving factors. Flood peak flows, flood frequency at different return periods, and potential driving forces are analyzed in this study. The peak flow of six gauging stations, with a catchment area ranging from 169 - 124,108 km2 and sufficient observed streamflow data, was selected to develop threshold (3rd quartile) magnitude and frequency (POTF) that occurred over ten years of records. Sixteen Potential climatic, watershed and human driving factors of floods in the study area were identified and analyzed with GIS, Pearson’s correlation, and Principal Correlation Analysis (PCA) to select the most influential factors. Eight of them (MAR, DA, BE, VS, sand, forest AGR, PD) are identified as the most significant variables in the flood formation of the basin. Moreover, mean annual rainfall (MAR), drainage area (DA), and lack of forest cover are explored as the principal driving factors for flood peak discharge in Wabi-Shebele River Basin. Finally, the study resulted in regression equations that helped plan and design different infrastructure works in the basin as ungauged catchment empirical equations to compute QMPF, Q5, Q10, Q50, and Q100 using influential climate, watershed, and human driving factors. The results of these empirical equations are also statistically accepted with a high significance correlation (R2 > 0.9).