Machine vision-based autonomous road hazard avoidance system for self-driving vehicles.

The resolution of traffic congestion and personal safety issues holds paramount importance for human's life. The ability of an autonomous driving system to navigate complex road conditions is crucial. Deep learning has greatly facilitated machine vision perception in autonomous driving.

Study on a risk model for prediction and avoidance of unmanned environmental hazard.

Comprehensive research is conducted on the design and control of the unmanned systems for electric vehicles. The environmental risk prediction and avoidance system is divided into the prediction part and the avoidance part. The prediction part is divided into environmental perception, environmental risk assessment, and risk prediction.

Fabry-Perot etalon-based ultraviolet trifrequency high-spectral-resolution lidar for wind, temperature, and aerosol measurements from 0.2 to 35 km altitude.

A novel ultraviolet trifrequency high-spectral-resolution lidar (HSRL) based on a triple Fabry-Perot etalon (FPE) and polarization discrimination technique is proposed, to the best of our knowledge, for measuring atmospheric wind, temperature, and aerosol optical properties simultaneously from the troposphere to low stratosphere. The measurement principle of wind speed, temperature, and aerosol is analyzed, and the structure of the proposed HSRL is designed. The parameters of the triple FPE are optimized.