COMPREDICT
Click to visit website
About
COMPREDICT provides virtual sensor solutions for the automotive industry, specifically targeting Original Equipment Manufacturers (OEMs) and Tier 1 suppliers. The platform replaces expensive or complex physical hardware sensors with purely software-based alternatives. By leveraging existing vehicle data—such as wheel speed, torque, and battery voltage—the tool uses machine learning and signal processing to estimate values that would normally require dedicated physical instrumentation. This transition to software-defined mobility helps manufacturers reduce vehicle weight, simplify the bill of materials, and lower overall production costs. The technology works through a three-step process: data acquisition from standard vehicle buses, calibration of the specific virtual sensor model, and finally, deployment either in the cloud or directly within the vehicle's embedded system stack. Key use cases include monitoring wheel forces, damper forces, driveshaft torque, and vehicle mass. By using sensor fusion and embedded AI, COMPREDICT allows for real-time monitoring of component health. This enables right-sizing of components during development and provides the necessary data for predictive maintenance strategies once the vehicle is on the road. This tool is designed for vehicle architecture teams, software-defined vehicle (SDV) engineers, and digital business units within the automotive sector. It is particularly valuable for fleet management companies looking to optimize maintenance schedules and reduce consumption costs. Because the solutions are deployable across both passenger cars and heavy commercial vehicles, it serves a wide range of industrial applications. Tier 1 equipment suppliers also benefit by adding measurement capabilities to their components without the added cost of physical sensor integration. What sets COMPREDICT apart is its deep integration of automotive engineering with data science. Founded as a spin-off from TU Darmstadt, the company combines academic rigor with industrial-scale application. Unlike general-purpose AI platforms, it focuses exclusively on chassis and powertrain domains, providing highly specialized models for tire and brake wear. Their partnership with major industry players like the Renault Group and backing from Toyota's Woven Capital underscores the reliability and scalability of their virtual sensor technology in a production environment.
Pros & Cons
Reduces vehicle weight and bill-of-materials by replacing physical sensors with software.
Supported by major industry leaders like Toyota and the Renault Group.
Applicable to both passenger vehicles and heavy commercial trucks.
Enables predictive maintenance by monitoring component health in real-time.
Offers a dedicated demo vehicle for physical technology validation at your facility.
Requires access to existing vehicle data streams for calibration and operation.
Initial setup involves a multi-step data acquisition and calibration process.
Pricing information is not publicly available and requires direct contact.
Use Cases
OEM engineers can replace expensive physical sensors with software to reduce production costs and vehicle weight.
Fleet managers can monitor tire and brake wear across their vehicles to optimize maintenance schedules and reduce downtime.
SDV development teams can unlock new vehicle features by integrating embedded AI into existing chassis and powertrain domains.
Tier 1 suppliers can add smart monitoring capabilities to their components without redesigning physical hardware.
Automotive researchers can use the COMPREDICTOR demo car to validate virtual sensor performance in real-world driving conditions.
Platform
Task
Features
• sensor fusion technology
• predictive maintenance insights
• real-time vehicle mass sensing
• software-defined vehicle (sdv) support
• cloud & edge deployment
• tire and brake wear monitoring
• embedded ai integration
• virtual sensor calibration
FAQs
How do virtual sensors work without hardware?
They use machine learning and signal processing to transform existing vehicle signals, like speed and battery voltage, into virtual measurements. This allows the system to estimate physical forces or wear levels without needing a dedicated physical hardware component.
Where can these virtual sensors be deployed?
The technology is highly flexible and can be integrated directly into a vehicle's embedded SDV stack for real-time edge processing. Alternatively, it can be hosted in a cloud environment to provide insights for fleet-wide analysis and aftersales business.
What types of vehicles are supported?
COMPREDICT's solutions are compatible with a wide range of vehicles, including standard passenger cars and heavy-duty commercial trucks. The software is designed to work across different powertrain domains and chassis configurations.
Can these sensors replace physical testing equipment?
Yes, they are specifically designed to replace traditional hardware sensors like wheel force transducers during the development and testing phases. This reduces the complexity of automotive testing and allows series vehicles to become as smart as development vehicles.
Pricing Plans
Enterprise
Unknown Price• Virtual sensor calibration
• Embedded SDV stack integration
• Cloud-based fleet monitoring
• Component health analytics
• Data acquisition support
• Custom sensor model development
• Access to COMPREDICTOR demo vehicle
• Hardware sensor replacement
Job Opportunities
Intern Tire Wear
Optimize vehicle performance and reduce hardware costs for OEMs and Tier 1s using AI-powered virtual sensors to monitor tire wear, mass, and component health.
Benefits:
1,000 € per month
Education Requirements:
Bachelor or Master student in Mechatronics or Mechanical Engineering
Experience Requirements:
Knowledge of vehicle dynamics and signal processing
Proficiency in Matlab/Simulink and Python for data analysis and scripting
Experience with time-series data analysis is a plus
Other Requirements:
Strong analytical and problem-solving mindset
Interest in innovative automotive technologies development
Responsibilities:
Collaborate hand-in-hand with Data-Scientists to support the development of a cutting-edge automotive solution
Understand and modify an existing approach to adapt to challenges from low frequency time series
Imagine, implement and evaluate different approaches for this Virtual Sensor
Contribute to an exploratory phase of a Virtual Sensor to shape a new market positioning
Show more details
DevOps Engineer
Optimize vehicle performance and reduce hardware costs for OEMs and Tier 1s using AI-powered virtual sensors to monitor tire wear, mass, and component health.
Education Requirements:
Degree in Computer Science, IT, or a related field
Experience Requirements:
3+ years of hands-on experience in infrastructure design and operations
Strong knowledge of Docker and Kubernetes in production environments
Proven experience building and maintaining CI/CD pipelines
Proficiency with Infrastructure as Code tools (e.g., Terraform, CloudFormation, or Pulumi)
Other Requirements:
Solid understanding of security best practices and compliance standards
Experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack, Datadog)
Fluent in English; excellent communication and collaboration skills
Responsibilities:
Architect, deploy, and maintain highly available, secure, and scalable production clusters
Develop and optimize containerized applications using Docker and Kubernetes
Design, implement, and maintain end-to-end CI/CD pipelines
Establish and enforce IaC principles using tools such as Terraform, CloudFormation, or Pulumi
Integrate security best practices into all DevOps processes
Show more details
MLOps Engineer
Optimize vehicle performance and reduce hardware costs for OEMs and Tier 1s using AI-powered virtual sensors to monitor tire wear, mass, and component health.
Experience Requirements:
At least 2 years working experiences in modern DevOps practices and microservice architecture
Expertise in Kubernetes and containerization technologies
Hands-on experience with platforms such as KubeFlow, Kserve, or equivalent
Experience in ML Experimentation and registry platforms such as W&B or MLFLow
Other Requirements:
Understanding of time series modeling and its data requirements
Familiar with ML/NN frameworks
Fluent in both written and spoken English; German is a plus
Familiar with AWS or other cloud service providers is a plus
Responsibilities:
Design and maintain scalable pipelines for deploying machine learning models
Ensure models are securely integrated into production environments with minimal latency
Implement monitoring systems to track model performance and flag issues
Develop methods to evaluate and compare the performance of different models
Automate processes for validating model accuracy and consistency
Show more details
Ratings & Reviews
No ratings available yet. Be the first to rate this tool!
Featured Tools
adly.news
Connect with engaged niche audiences or monetize your subscriber base through an automated marketplace featuring verified metrics and secure Stripe payments.
View Details
Image to Image AI
Transform photos and videos using advanced AI models for face swapping, restoration, and style transfer. Perfect for creators needing fast, professional visuals.
View Details
Nano Banana
Edit and enhance photos using natural language prompts while maintaining character consistency and scene structure for professional marketing and digital art.
View Details
Nana Banana Pro
Maintain perfect character consistency across diverse scenes and styles with advanced AI-powered image editing for creators, marketers, and storytellers.
View Details
Kling 4.0
Transform text and images into cinematic 1080p videos with multi-shot storytelling, character consistency, and native lip-synced audio for professional creators.
View Details
AI Seedance
Generate 15-second cinematic 2K videos with physics-based audio and multi-shot narratives from text or images. Ideal for creators and marketing teams.
View Details
Mistrezz.AI
Engage in immersive NSFW roleplay and ASMR voice sessions with adaptive AI companions designed for structured escalation, fantasy scenarios, and personal connection.
View Details
Seedance 3.0
Transform text prompts or static images into professional 1080p cinematic videos. Perfect for creators and marketers seeking high-quality, physics-aware AI motion.
View Details
Seedance 3.0
Transform text descriptions into cinematic 4K videos instantly with ByteDance's advanced AI, offering professional-grade visuals for creators and marketing teams.
View Details
Seedance 2.0
Generate broadcast-quality 4K videos from simple text prompts with precise text rendering, high-fidelity visuals, and batch processing for content creators.
View Details
BeatViz
Create professional, rhythm-synced music videos instantly with AI-powered visual generation, ideal for independent artists, social media creators, and marketers.
View Details
Seedance 2.0
Generate cinematic 1080p videos from text or images using advanced motion synthesis and multi-shot storytelling for marketing, social media, and creators.
View Details