The Study On Target Visibility Of Mesopic Vision Based On Virtual Driving Scene

Good road lighting is critical to reducing the incidence of traffic accidents.Small target visibility is a more effective indicator than luminance and illuminance and is recognized by the Commission Internationale de L'Eclairage(CIE)and the North American Lighting Society and implemented in the United States.However,the current small target visibility criteria ignores the effects of driving load and small target mobility.Based on the virtual driving experiment platform,this paper studies on the small target visibility of mesopic vision under different driving loads.A 2D virtual driving experimental platform capable of providing driving environment and driving load was established.Based on the platform,the influence of driving load on reaction time was studied.The results show that the driving load has a significant effect on the reaction time,and the driving load will cause the reaction time to become longer.In addition,contrast,lane and distance have a significant effect on reaction time.It also proves the feasibility and effectiveness of the virtual driving experimental platform.The 3D virtual driving experiment platform was established,which overcomes the shortcomings of the lack of motion authenticity of the small target of the 2D platform,and it can provide a more realistic night driving scene.Based on the 3D platform,this paper studies the effects of visual factors such as driving load and contrast on response time,detection rate and MVP(detection rate/reaction time).Studies have shown that driving load and various visual factors have a significant impact on reaction time and MVP,and there is interaction between the main factors.In addition to the distance that small targets appear,driving loads and other visual factors have a significant impact on the detection rate.The smaller the driving load,the higher the absolute value of the contrast,and the smaller the reaction time,the higher the detection rate and the larger the MVP.The closer the distance is,the smaller the reaction time is,and the larger the MVP is.Contrast has a logarithmic relationship with reaction time,detection rate,and MVP.On this basis,this paper studies the changes of reaction time and detection rate under different driving speeds.The results show that speed has a significant effect on reaction time and detection rate.As the speed increases,both the reaction time and the detection rate decrease.Based on the deep learning algorithm,a prediction model of reaction time is established.The model can predict the response time of small targets by human eyes through speed,contrast,lane and distance.Based on the model,the minimum collisions to be maintained at different speeds and detection rates are calculated.It can be seen that the safety distance specified by the Chinese Road Traffic Safety Law is greater than the collision distance.A more reliable and practical small target visibility model considering driving load and different speeds is established.The model obtains the visibility level threshold corresponding to 85% and 95% detection rate at different speeds.The results show that the current visibility level of 7 can meet the 85% detection rate requirement.For a 95% detection rate,when driving speeds of 60,80,and 100 km/h,the corresponding visibility levels are 13,27,and 40,respectively,well above 7.