Autobrains
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About
Autobrains is an automotive technology company specializing in the development of advanced artificial intelligence for autonomous driving and advanced driver assistance systems (ADAS). At the core of its offering is Liquid AI, a proprietary technology that moves away from traditional, data-heavy supervised learning models. Instead, it utilizes signature-based self-learning to create a more flexible and efficient perception system. This approach allows vehicles to interpret their surroundings and respond to complex scenarios with a level of intelligence the company refers to as AD2.0, focusing heavily on safety, reliability, and cost-effectiveness for the mass market. The technology functions by employing a self-learning architecture that does not rely on massive, manually labeled datasets. Instead, it identifies patterns and signatures in the environment, which enables the system to detect and manage a vast array of edge cases that often baffle conventional AI systems. One of the primary practical advantages of this system is its hardware-agnostic nature; it is designed to be compatible with various sensors and System-on-Chips (SoCs). For entry-level safety features, the technology is optimized to run on as little as 1 to 2 TOPS, making high-level safety accessible even for budget-friendly vehicle segments. Autobrains is primarily designed for automotive original equipment manufacturers (OEMs), Tier 1 suppliers, and electric vehicle startups looking to implement scalable autonomous features. It is particularly well-suited for companies that need to meet rigorous global regulations, such as the General Safety Regulation (GSR) and New Car Assessment Program (NCAP) standards, without incurring the extreme costs associated with high-end computational hardware. The solutions range from smart camera systems for basic ADAS to high-performance configurations for Level 4 autonomous driving projects, catering to a wide spectrum of automotive development needs. What distinguishes Autobrains from competitors is its unique balance of low computational demand and high perception accuracy. While many autonomous driving solutions require expensive, energy-hungry processors, Autobrains provides a path to affordable autonomous driving through its 250 plus patents and a library of up to 400,000 driving skills. By solving the challenges of edge cases, drivers trust, and explainability through its Real-World Liquid AI, the tool provides a transparent and robust alternative to black box deep learning models, making it a disruptive force in the future of automotive mobility.
Pros & Cons
Highly efficient performance requiring only 1-2 TOPS of compute power for entry-level solutions.
Patented Liquid AI architecture is protected by over 250 distinct patents.
System is completely sensor and SoC agnostic, offering high integration flexibility for OEMs.
Superior edge-case detection capabilities compared to traditional supervised learning models.
Ensures full compliance with international GSR regulations and NCAP safety standards.
Requires a direct inquiry for pricing and custom implementation details.
The technology is complex and may require a strategic partnership for full-scale deployment.
Demo rides are limited to specific geographic regions mentioned during the inquiry process.
Use Cases
Automotive OEMs can integrate the Smart Camera solution to achieve GSR compliance and NCAP safety standards in entry-level vehicles at a low cost.
Electric vehicle manufacturers can utilize the low-compute Liquid AI to maximize vehicle range by reducing the energy consumption of autonomous driving systems.
Strategic partners and L4 project teams can leverage the signature-based self-learning architecture to solve complex edge-case scenarios that traditional AI fails to navigate.
Platform
Features
• edge case detection
• 400,000+ driving skills
• 1-2 tops low-compute requirement
• gsr and ncap compliance
• soc-agnostic perception
• sensor-agnostic perception
• liquid ai architecture
• signature-based self-learning
FAQs
What is Liquid AI technology?
Liquid AI is Autobrains' patented signature-based self-learning architecture designed to handle complex edge cases in autonomous driving. It reduces the need for massive labeled datasets and high compute power compared to traditional AI.
Does the system work with existing hardware?
Yes, the solution is SoC and sensor agnostic, meaning it can be integrated with various system-on-chips and sensor configurations. This flexibility allows automotive manufacturers to implement the technology without being locked into specific hardware vendors.
Is Autobrains compliant with safety regulations?
The technology is specifically engineered to meet global safety standards, including General Safety Regulation (GSR) and New Car Assessment Program (NCAP) requirements. This ensures that vehicles using the system are eligible for high safety ratings.
What compute power is required for the entry-level solution?
The Smart Camera solution is highly efficient, requiring only low compute power between 1-2 TOPS. This makes it an ideal choice for affordable, entry-level ADAS implementations in mass-market vehicles.
Pricing Plans
Enterprise
Unknown Price• Liquid AI access
• Scalable ADAS solutions
• GSR and NCAP compliance
• Sensor agnostic integration
• Low compute requirements (1-2 TOPS)
• Edge case detection
• Signature-based self-learning
• Access to 400,000+ skills
Job Opportunities
Path Planning Algo Team Leader
Deploy cost-effective, regulatory-compliant autonomous driving with self-learning AI that masters edge cases using minimal compute power for automotive OEMs.
Experience Requirements:
5+ years in autonomous driving, robotics, planning, navigation, or control systems
Strong expertise in motion planning, trajectory generation, or control theory
Excellent C++/Python skills with real-time system experience
Demonstrated team leadership or tech-lead responsibility
Experience in automotive production environments (advantage)
Other Requirements:
Strong system-level thinking, ownership, and willingness to be hands-on
Responsibilities:
Lead the design and development of Planning & Control algorithms
Build and mentor a top-performing engineering team
Develop motion planning, trajectory optimization, and behavior layers
Own robustness, performance, and safety across the decision pipeline
Define clean interfaces between perception, prediction, world model, and actuation
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Senior Path Planning Algorithm Engineer
Deploy cost-effective, regulatory-compliant autonomous driving with self-learning AI that masters edge cases using minimal compute power for automotive OEMs.
Experience Requirements:
5+ years of experience in autonomous driving / robotics / motion planning / navigation / real-time systems
Strong expertise in Path Planning and/or optimization-based planning methods
Strong C++ skills (Python is a plus)
Experience working with real-time systems and performance-critical code
Experience in automotive production environments
Other Requirements:
Strong problem-solving skills
End-to-end ownership
Ability to work independently
Responsibilities:
Design and develop Path Planning algorithms
Build planning components such as path generation and refinement
Develop and improve planning modules integrated with our world model
Implement and optimize algorithms in C++ with real-time performance constraints
Work closely with perception, prediction, and control teams
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VP Business Development
Deploy cost-effective, regulatory-compliant autonomous driving with self-learning AI that masters edge cases using minimal compute power for automotive OEMs.
Experience Requirements:
10+ years in automotive, mobility, or AI partnerships
Proven track record of complex deals with OEMs and major technology companies
Other Requirements:
Strong technical fluency in AI, perception, or autonomy
Entrepreneurial mindset with ability to operate in a fast-moving startup
Responsibilities:
Originate and structure strategic technology and commercial partnerships
Translate technical capabilities into practical, scalable business models
Lead multi-party collaborations across automotive and AI ecosystems
Work closely with product and engineering to align partnerships with roadmap
Represent the company in executive-level engagements and industry forums
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Ratings & Reviews
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