Search

In order to solve the problem of cumbersome external parameter calibration of solid-state lidar and overcome the difficulty in verifying its calibration results, a simple calibration and verification method for solid-state lidar is proposed. This method designs a simple calibration field and markers, uses the markers to complete the angle calibration of the lidar, and completes the distance calibration of the lidar by using the surveying method, so as to reduce the correlation of the external parameters of the angle and the distance, and to ensure the accuracy of the calibration result. Finally, the error of external parameter is obtained through comparison and calculation, and the calibration result of the lidar is verified. The experimental results show that the method proposed is reasonable and effective, and greatly reduces the equipment preparation time and cost required for lidar calibration and verification results, which has a certain application value.

Combined with the current research status of the intelligent vehicle decision-making methods at home and abroad, this paper classifies and summarizes decision-making methods from four aspects: decision input and output, environment interaction, and algorithm types. Besides, it analyzes their advantages and disadvantages, and evaluates applicable scenarios. Moreover, it surveyes the common data sets and current evaluation standards which are used for decision-making researches. Furthermore it discusses the technical difficulties faced by current decision-making methods and future development trends.

Existing decision-making methods for intelligent vehicles do not consider factors such as the right of way information, polite driving of the vehicle, and limited perception range of the vehicle, which may easily lead to safety hazards in merging scenarios. Aimed at these problems, a Stackelberg-game-based decision-making method is proposed. This method constructs a game model combining the right of way and conducts parametric modeling of the merging scenarios. Then, the cooperation factor is introduced to design the corresponding profit function. Finally, the vehicle decision-making solution framework is designed to achieve the maximum value of decision-making benefits in this scenario. The experimental results illustrate that the proposed method can effectively improve the accuracy of vehicle decision-making behavior prediction on the datasets and the decision-making robustness in a high traffic density environment.