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In the new development stage of comprehensively building a socialist modern state, it is imperative to adhere to the guidance of Xi Jinping Thought on Socialism with Chinese Characteristics for a New Era, comprehensively summarize China's practical experiences in economic development, strengthen research on capital issues, construct theories of socialist political economy with Chinese characteristics regarding capital and provide scientific theoretical guidance for further promoting the positive role of various types of capital while preventing and overcoming their negative effects, which is a major theoretical issue and a glorious task for the theoretical and economic circles in China.

From the perspective of Marx's theory on capital and historical development, modern capital represents the organizational mode of socialized mass production and market economy. It serves as both the economic foundation of bourgeois society and a tool for socialist economic development.

The market economy represents an inevitable historical stage and form of socialist economic development, necessitating the adoption of capital as an organizational form within socialist economies.

The utilization of capital to advance a socialist economy is a remarkable achievement by the CPC and Chinese people, representing a significant innovation in both theory and practice. The role of capital is inherently dual under any social condition. In the context of a socialist system, capital can play a positive role effectively, and its behavior can be guided and regulated correctly to curb its negative or even destructive impact.

Intelligent and connected vehicle technology is in the ascendant. High-level autonomous driving places more stringent requirements on the accuracy and reliability of environmental perception. Existing research works on multitarget tracking based on multisensor fusion mostly focuses on the vehicle perspective, but limited by the principal defects of the vehicle sensor platform, it is difficult to comprehensively and accurately describe the surrounding environment information.

In this paper, a multitarget tracking method based on roadside multisensor fusion is proposed, including a multisensor fusion method based on measurement noise adaptive Kalman filtering, a global nearest neighbor data association method based on adaptive tracking gate, and a Track life cycle management method based on M/N logic rules.

Compared with fixed-size tracking gates, the adaptive tracking gates proposed in this paper can comprehensively improve the data association performance in the multitarget tracking process. Compared with single sensor measurement, the proposed method improves the position estimation accuracy by 13.5% and the velocity estimation accuracy by 22.2%. Compared with the control method, the proposed method improves the position estimation accuracy by 23.8% and the velocity estimation accuracy by 8.9%.

A multisensor fusion method with adaptive Kalman filtering of measurement noise is proposed to realize the adaptive adjustment of measurement noise. A global nearest neighbor data association method based on adaptive tracking gate is proposed to realize the adaptive adjustment of the tracking gate.