As a global leader in automotive safety systems, Autoliv is dedicated to advancing occupant safety through innovative design. A key challenge in crash tests for novel seatbelt designs lies in analyzing impact-over-time data, where engineers manually identify anomalies, such as excessive force, and determine causes and fixes. With this opportunity, we seek to automate test data analysis using AI-based solutions, enabling faster identification of issues and optimal improvements for next-generation seatbelt systems.
Opportunity overview
By leveraging artificial intelligence, Autoliv could transform seatbelt crash test analysis, enabling faster and more precise detection of anomalies, reducing resource demands, and accelerating design iterations. AI-powered tools can analyze data in real time, uncover patterns, enhancing efficiency and provide actionable recommendations.
To achieve this, we seek partners with a strong track record in AI-driven analytics and experience working with complex, high-volume datasets. Ideal partners would bring innovative approaches to data visualization, anomaly detection, and predictive modeling. Additionally, Autoliv values collaborators who understand the challenges of automotive safety solutions and share a commitment to rigorous quality standards. Partners should also be capable of scaling solutions to meet the demands of global operations and have a collaborative mindset to work closely with design teams, safety experts, and engineers.
Your opportunity with Autoliv
Partnering with Autoliv provides a unique opportunity to collaborate with a global leader in automotive safety systems and contribute to advancing safety standards in a critical industry. We are mostly interested in off-the-shelf solutions that can be quickly adapted and scaled to meet industry needs. Partners gain access to real-world crash test data, industry-leading expertise, and extensive operational environments to refine, validate, and deploy their innovations. This collaboration enables emerging companies to accelerate their technological growth while addressing the complex challenges of automotive safety and industrial operations.
Examples we're looking for
Anomaly detection
Solutions automatically detecting anomalies in crash test data, such as excessive force, enabling faster identification of issues and reducing the need for manual inspections.
Root cause analysis
Solutions utilizing machine learning to analyze detected anomalies, uncover their root causes, and provide deeper insights into underlying issues.
Data-driven recommendations
AI-powered solutions that generate actionable insights from crash test data, helping engineers refine designs, enhance safety, and accelerate decision-making processes.
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