Euro NCAP Safety Results for the Micra & Qashqai

The Nissan Micra, positioned in the City and Supermini class and recognised as the sister model to the Renault 5 assessed by Euro NCAP in 2024, recorded an 80% score (32.3 points) for Adult Occupant protection. In the frontal offset impact test, the passenger cabin maintained its structural integrity, and assessments showed that whiplash protection in rear-end collisions was strong for both front and rear seat occupants.

Nissan has announced the latest Euro NCAP safety assessment results for its Micra and Qashqai models. According to its press release, the Micra has achieved a child occupant protection score of 80% (39.5 points), while the Qashqai has scored 85% (41.8 points) in the same category. These results underline Nissan’s continued emphasis on ensuring strong protection for younger passengers.

While the Nissan press release focuses on the headline child-occupant numbers, these high scores suggest that both vehicles benefit from well-engineered crash-protection systems in scenarios involving children, which are among the most critical and challenging tests in vehicle safety assessments.

By highlighting these Euro NCAP results, Nissan is reinforcing its reputation for occupant safety, particularly for families, and demonstrating that both the Micra and Qashqai deliver very solid protection for their youngest passengers.

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Accelerating Development with AI: Extending the Nissan-Monolith Partnership

In a second announcement, Nissan revealed that it is deepening its collaboration with UK-based artificial intelligence firm Monolith, extending their strategic partnership through to 2027. The stated purpose of this extended relationship is to reduce Nissan’s reliance on physical testing during vehicle development, by using AI to simulate and predict test outcomes.

How AI Is Applied to Testing

At the heart of this innovation is the use of Monolith’s machine-learning models at Nissan Technical Centre Europe in Cranfield. These models have been trained on decades of historical test data, enabling them to predict crash-test performance and the outcomes of other physical evaluations with a high degree of accuracy. By leveraging these predictions, Nissan can dramatically reduce the number of physical prototypes it needs to build, thereby saving both time and resources.

A concrete example presented in the announcement relates to tests on bolt-joint torque. Rather than performing every physical test, Nissan engineers used Monolith’s AI to recommend optimal torque ranges and to prioritise which physical tests to run. This approach yielded a 17 per cent reduction in physical testing compared to a traditional, non–AI-assisted process.

Strategic and Operational Gains

Nissan projects that, by scaling up this AI-driven testing methodology across its European model range, it could halve the total time required for physical testing across the development programme. According to Emma Deutsch, Director of Customer-Oriented Engineering & Test Operations at Nissan, the use of AI allows engineers to “simulate and validate vehicle performance,” reducing dependency on physical prototypes. This in turn frees up engineering teams to focus on design optimisation, problem-solving and creative development, all while reducing resource use and lowering the environmental footprint of testing.

To support this vision, Monolith offers specific tools: a Next Test Recommender, which helps suggest which physical test would be most useful next, and an Anomaly Detector, which identifies unexpected or unusual predicted outcomes in simulations that might warrant further investigation.

Dr Richard Ahlfeld, CEO of Monolith, emphasises that their objective is to “empower engineers with AI tools that unlock smarter, faster product development.” He notes that the partnership with Nissan is a clear demonstration of how machine-learning technology can drive efficiency, reduce resource consumption, and speed up development cycles without compromising the quality and safety of the final product.

Broader Strategic Context

This AI-driven testing strategy is closely aligned with Nissan’s wider RE:Nissan turnaround plan, which places a strong emphasis on innovation, operational efficiency, and reducing time-to-market for new vehicles. By significantly reducing the need for physical prototyping, Nissan can potentially bring new models to market more quickly and cost-effectively, all while maintaining high standards of safety and performance.

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