Driving Assistance System For Dangerous Goods Transportation Vehicles Based On ZYNQ

With the development of my country’s industrialization,the number of dangerous goods produced and transported is increasing day by day,among which chemical companies account for more than 90%of dangerous goods transport.With the migration of chemical companies from coastal areas to inland areas,the mode of transport of dangerous goods has also changed.Road transportation has gradually become the main method,with an annual transportation volume of about 300 million tons.Road transportation takes a long time and dangerous goods have high risks.Once an accident occurs,it will spread to a large area and cause serious consequences.Therefore,it is necessary to install driver assistance systems on dangerous goods transportation vehicles to improve transportation safety.Existing driving assistance systems are generally applied to passenger vehicles.Taking into account the special needs of dangerous goods vehicles,further improvements and enhancements are needed,especially in terms of information processing speed and detection accuracy.The research takes Xilinx’s ZYNQ SOC as the core,and uses FPGA+ARM architecture to accelerate the image processing algorithm to improve the real-time performance of the system.The research will mainly focus on two parts:lane departure collision warning and fatigue driving detection.First of all,the lane departure warning system and fatigue detection will monitor the driver’s driving behavior and status in real time.The front and rear collision warning will monitor the driving environment of dangerous goods vehicles,remind the surrounding vehicles,and remind the driver of voice and vibration.Helps to further improve driving safety.This paper integrates the three functions of lane departure warning system,fatigue driving detection system and collision warning system to ensure the safe driving of drivers technically.Secondly,starting from the real-time requirements of dangerous goods transportation vehicles for assisted driving systems,the study adopts the FPGA+ARM software and hardware cooperation method,and uses FPGA parallel processing and ARM’s easy-to-develop features to complete the accelerated processing of image algorithms.The pipeline structure is formed by inserting registers in the design,which can effectively improve the detection and decision-making speed of the system.Finally,the hardware system and software system are constructed,and the design is verified through experiments.The experimental results show that the image processing frame rate of the lane departure and fatigue detection system is greatly improved,and the detection accuracy meets the design requirements.The system can effectively improve the driving safety of dangerous goods transportation vehicles from both active and passive safety aspects.The design takes into account both performance and cost,and has good real-time performance and detection accuracy.