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The Growing Concern of Untrusted Lidar Technology: A Threat to National Security and Global Competitiveness
The exponential growth in connected and automated systems has led to a surge in demand for sensor technology, particularly lidar, which uses pulsed light to map the surrounding environment. This technology is crucial for autonomous vehicles, airports, infrastructure mapping, and other connected systems. Analysts project global automotive lidar revenues to reach $332 million in 2022, with Chinese companies like Hesai dominating the market.
However, the rapid expansion of Chinese lidar companies raises concerns about national security threats. Chinese firms benefit from state subsidies and procurement preferences, leading to fears of data exploitation by malign actors. Chinese technology companies are subject to national security laws that could compromise sensitive data, posing risks to critical infrastructure and cybersecurity.
In response to these concerns, the U.S. government has taken steps to investigate Chinese lidar firms for ties to the military. Hesai, a prominent player in the market, was recently added to the Department of Defense’s list of Chinese military-linked companies. Despite objections from Hesai, evidence suggests their technology has been used in Chinese military vehicles.
To address these security risks, policymakers are considering additional regulations on untrusted sensor technology companies and investing in domestic alternatives to reduce dependence on foreign technology. Scrutinizing lidar and other emerging technologies from countries of concern is crucial to safeguarding national security and maintaining global competitiveness.
As the U.S. navigates the challenges posed by the growing presence of Chinese lidar companies, strategic actions must be taken to protect sensitive data and prevent reliance on potentially compromised systems. By investing in trusted domestic technologies and implementing stringent regulations, the U.S. can mitigate security risks and ensure a secure future for automated systems.