Meet the NEC Labs America Intern Helping to Make Autonomous Vehicles Safer and More Secure

There’s much more to autonomous vehicle security than locking a car door.

This summer, Kaiyuan Zhang, a 3rd-year computer science Ph.D. student at Purdue University, joined NEC Labs America’s popular intern program to help advance research around autonomous vehicle security.

Each year, nearly 50 Ph.D. candidates join NEC Labs America’s innovative program, which centers on a collaborative environment where interns work directly with senior researchers and potential end-user customers. They focus on advancing cutting-edge technologies at the intersection of machine learning and science that will improve how the world can become more responsive to the needs of individuals. This 12-week program is run out of NEC Labs America’s research facilities in Princeton, NJ, and San Jose, California.

Kaiyuan Zhang NEC Labs America Intern

In addition to an immediate supervisor, each intern is assigned to a specific mentor. Kaiyuan’s mentor this summer is Francesco Pittaluga, a researcher in the Media Analytics Department at NEC Labs America focused on Computer Vision, Machine Learning, and Computational Photography Biography.

According to Pittaluga, “The intern program is not only an excellent opportunity for Ph.D. students to gain experience working in an industrial research lab, but it is also a huge asset for NEC Labs. Interns like Kaiyuan help introduce new insights and ways of thinking to our group. His background in security has been critical for developing an adversarial robustness framework for training and evaluating autonomous driving systems.”

Interns like Kaiyuan quickly become part of a project team applying innovative technology to industry-leading concepts.

“My research interests mainly focus on security and privacy in machine learning,” said Kaiyuan. “With this expertise, I’m able to provide unique perspectives in our collaborative work with other researchers on NEC Labs America’s Media and Analytics team working on improving the robustness of the autonomous driving system.”

This work extends well beyond the physical security of autonomous vehicles. As with most emerging technologies, securing the data generated, processed, and used by autonomous cars is complex. Kaiyuan is helping to develop safety-critical driving scenarios that improve the robustness and safety of autonomous driving systems.

Take, for example, the multitude of information captured by the cameras and sensors equipped on autonomous vehicles. They record a vast array of data from the surrounding environment, everything from other vehicles and traffic signals to hazard cones and pedestrians, even down to lane markings. Kaiyuan is actively involved in automatically generating safety-critical driving scenarios, which is crucial in enhancing the real-world safety of autonomous driving.

Autonomous Vehicle

According to Kaiyuan, there have already been instances of security breaches by malicious entities that manipulate how a car is programmed to respond in specific situations. This includes programming a vehicle to exhibit unreasonable behavior upon detecting orange hazard cones. In another case, adversaries have strategically placed adversarial patterns on stop signs, compelling the vehicle to take an action contrary to stopping. These scenarios carry not just potential danger but could also escalate to lethal circumstances. Research conducted by Kaiyuan and his team is meticulously targeted toward enhancing the robustness of the autonomous driving system against such security threats.

Kaiyuan also hopes to return to the NEC Labs Intern program next summer to continue collaborating on cutting-edge technologies.

Please click here to learn about our NEC Labs America Intern Program.

Read Our News Posts

Princeton Interns 2024

Summer Interns 2024

Learn about the amazing group of interns who joined us at Princeton and San Jose campuses this summer. Their hard work, fresh perspectives, and dedication have truly made an impact across the board, from cutting-edge research projects to innovative software development initiatives.
Introducing the Trustworthy Generative AI Project Pioneering the Future of Compositional Generation and Reasoning Blog Post

Introducing the Trustworthy Generative AI Project: Pioneering the Future of Compositional Generation and Reasoning

We are thrilled to announce the launch of our latest research initiative, the Trustworthy Generative AI Project. This ambitious project is set to revolutionize how we interact with multimodal content by developing cutting-edge generative models capable of compositional generation and reasoning across text, images, reports, and even 3D videos.
Introducing Our New Project Time Series Language Model for Explainable AI Blog Post

Introducing Our New Project: Time Series Language Model for Explainable AI

Our new project, Time Series Language Model for Explainable AI, represents a significant leap forward in the field of forecasting and explainable AI. By combining advanced forecasting techniques with explainable AI, we are paving the way for a future where data-driven insights are not only accurate but also comprehensible and actionable.
Agentic LLMs for AI Orchestration Project Revolutionizing Complex Workflows

Agentic LLMs for AI Orchestration Project: Revolutionizing Complex Workflows

The development of Agentic LLMs for AI Orchestration represents a significant advancement in artificial intelligence. By seamlessly integrating computer vision, logic, and compute modules, our LLM is poised to revolutionize the way complex workflows are managed and executed. Supported by robust research and driven by innovative training methodologies, our agentic LLM sets a new standard in AI orchestration, offering unparalleled performance and adaptability.
The Evolution of Disciplines From Experimental Roots to Theoretical Frameworks Blog Post

The Evolution of Disciplines: From Experimental Roots to Theoretical Frameworks

Chris White discusses the evolution of scientific disciplines, which often begin with exploration driven by curiosity and experimentation. As fields grow in complexity and cost, they shift to theoretical frameworks that optimize research. This transition is crucial for sustainable progress. Currently, AI and ML remain largely experimental, highlighting the need for theoretical foundations to ensure development.
Multi-Agent Simulator for Carbon Neutrality

Multi-Agent Simulator for Carbon Neutrality: The Technology the World Has Been Waiting For

Today, each country, government, and enterprise are urged to take effective action to fight against climate change; however, an efficient method has not been found. Even a way to accurately calculate Scope 3 carbon emissions has yet to be developed. The technology of a multi-agent simulator could be an essential step in solving worldwide challenges. We interviewed the researchers about the details of this technology.