How AI is Making Driving Safer and Smarter

At a recent tech event, NEC showcased a cool tool that could change how we think about driving. Their AI Driving Diagnosis system takes regular dashcam footage and turns it into a chat about driving habits. But this isn't just another gadget. It uses a mix of video recognition AI and a large language model (LLM) to understand driving behavior. This means it doesn't just spot patterns—it explains them. For example, if a driver makes a risky lane change, the AI can point it out and suggest safer alternatives. The system was tested in a simulator. A participant drove while the AI analyzed speed, acceleration, and GPS data. It then generated a report highlighting things like abrupt braking or smooth turns. This report can be shared with insurers, fleet managers, or city transport agencies. The goal? To help organizations understand driver behavior and cut down on accidents and fuel costs. So, how does it work? NEC's tech is like a "video version of ChatGPT. " It uses over 100 visual recognition engines to detect objects, track motion, and understand the environment. This data is stored in a special database, ensuring the AI's explanations are based on facts. The result? A short, precise summary of driving footage, highlighting what was done well and what could be improved. But developing this tech wasn't easy. NEC faced three main challenges: understanding the user's intent, comprehending complex visual context, and generating accurate explanations. They solved these issues using LLMs, which helped create clear, concise summaries. For instance, the AI might say, "Your deceleration before intersections is abrupt; easing off earlier would improve safety and fuel efficiency. " This makes the feedback much easier to understand than a generic warning light. NEC's AI Driving Diagnosis is part of a bigger effort to build safe-mobility infrastructure. They've also developed a system that predicts the best network for connected vehicles, considering factors like traffic and weather. Together, these systems create a loop: video AI evaluates driving behavior, QoE prediction evaluates safe routes, and the LLM explains why changes matter. The potential uses for this tech are vast. Local governments can monitor public transport fleets, logistics companies can track delivery trucks, and insurance providers can integrate AI assessments into their products. NEC has already commercialized related services in Japan and is discussing pilot programs with various partners. Plus, the system is designed to handle sensitive data securely, maintaining privacy standards. So, why does this matter? Driver-behavior analytics already exist, but they usually stop at numbers and alerts. NEC's approach goes further by understanding context and explaining cause and effect in natural language. This turns data into coaching, helping drivers develop safer habits before a crash occurs. For insurers, it means smarter feedback and potentially lower claim costs. For fleet managers, it means objective performance metrics for multiple drivers. In the end, NEC's demo at CEATEC 2025 was short, but its implications are broad. By combining computer vision, network optimization, and generative AI, NEC is building a foundation for a safe-mobility ecosystem. This ecosystem doesn't just record how we drive—it helps us drive better. If trials with insurance and fleet partners are successful, the next wave of connected-vehicle services might go beyond tracking our trips. They could soon explain them, turning every drive into an intelligent feedback session.