Vehicle OS
Modern vehicle software platforms typically consist of an operating system and middleware to manage hardware and software resources, APIs for application development, services for connectivity, security, and user interface management, and consumer experiences such as navigation, infotainment, and voice assistant. Modern vehicles are also built based on a centralized hardware architecture consisting of compute units running infotainment, ADAS and AD software, and zonal controllers to manage all the control units and endpoints (sensors and actuators) in the vehicle.
Cloud-based tools streamline development and maintenance by providing scalable resources for testing, deployment, and updates, facilitating collaboration across geographies, and enabling data analytics for continuous improvement.
AI enhances vehicle software platforms by improving user experiences through personalized settings, optimizing vehicle performance and energy efficiency, and enabling advanced features like predictive maintenance and autonomous driving capabilities.
Cloud integration is crucial for the continuous improvement and maintenance of vehicle software. It allows for secure over-the-air software updates and patches, ensuring that ADAS and AD systems are always equipped with the latest algorithms and safety features. Additionally, cloud integration supports the management of data logs and performance metrics, enabling remote diagnostics and optimizations that enhance system reliability and efficiency.
Tools for Vehicle Intelligence
Advanced driver-assistance systems are technologies used in vehicles to enhance safety and driving. They rely on inputs from multiple data sources, including automotive imaging, lidar, radar, computer vision, and sensors to assist drivers with tasks like parking, lane keeping, and collision avoidance.
Automated driving is a technology involved in the development and deployment of systems that enable vehicles to drive themselves without human intervention. This encompasses the use of various technologies including sensors, software algorithms, and machine learning to perceive the environment, make decisions, and control the vehicle effectively and safely.
The Society of Automotive Engineers (SAE) defines 6 levels of driving automation, ranging from 0 (fully manual) to 5 (fully autonomous). Each level describes the degree of autonomy in the vehicle, indicating how much control the system has versus the human driver. These levels have been adopted by the U.S. Department of Transportation.
Level 0: No automation. Human performs all driving tasks. Level 1: Driver assistance. A single vehicle function is automated, such as cruise control. Level 2: Partial automation. Vehicle handles steering and acceleration but human can take control at any time. Level 3: Conditional automation. Environmental detection enables the vehicle to handle certain tasks, but human oversight is required. Level 4: High automation. Vehicle performs all tasks under specific circumstances, such as dedicated lanes. Human can take control at any time. Level 5: Full automation. Vehicle performs all driving functions in all conditions. No human intervention is required.
A comprehensive ADAS and AD development platform typically includes simulation, validation, and data management tooling. These components work together to enable rapid development, testing, and deployment of ADAS and AD software.
Essential testing types for ADAS and AD systems include real-world testing, test track testing, and virtual testing. These tests assess the reliability and effectiveness of the systems under various scenarios. Functional safety tests ensure that the systems react correctly to inputs and failures to validate their performance across different environments.
Simulation tools play a crucial role in vehicle software development by providing a safe, scalable, and cost-effective environment for testing and refining algorithms. They help teams model and simulate complex driving scenarios and sensor interactions that are impractical or risky to test in real-world conditions.
AI enables vehicles to make decisions in real time, learn from diverse environmental conditions, and improve through machine learning models, thereby enhancing the ability to handle complex and dynamic road situations.
The V-model is a structured approach commonly used in the automotive industry, particularly for safety-critical systems. It starts with product and system requirements, designing functionality, and results in the implementation of the software. Then, the software is tested with software-in-the-loop, hardware-in-the-loop, deployed, and validated for production.
With the traditional V-model for development, software integration is happening infrequently, and there are too many dependencies on hardware. Additionally, there are local, siloed tools and slow cycles leading to bugs and production delays. Applied Intuition's tooling, by contrast, enables more consistent updating and parallel communication across all development steps.
SDS for Automotive
An autonomy stack for passenger cars consists of layered software components that handle perception, decision-making, planning, and vehicle control. These systems support features ranging from driver assistance—like lane centering, adaptive cruise control, and traffic jam assist—to more advanced capabilities that require limited to no driver intervention. The stack is a critical part of developing and scaling ADAS and autonomy features across modern vehicles.
An end-to-end autonomy approach unifies the key components of an autonomous driving system—like perception, decision-making, and vehicle control—into one cohesive system. This approach feeds raw data from sensors (e.g., cameras, lidar, radar) into a neural network that's processed in a single integrated framework. This streamlines development for manufacturers, reduces system complexity, and improves the efficiency and reliability of the autonomous driving experiences.
For OEMs, E2E autonomy stacks enhance passenger vehicles' autonomous capabilities, increase safety, and ensure robust and reliable development for complex driving conditions, including urban environments and highways.
Yes, SDS for Automotive is designed for flexibility and can be integrated seamlessly with existing vehicle systems. Its modular architecture allows for customization to meet specific operational requirements, and it integrates deeply with vehicle architecture, including Vehicle OS and infotainment, enabling real-time HMI visualization across the stack. SDS for Automotive also integrates seamlessly with Applied Intuition's industry-leading tooling to streamline validation and accelerate development timelines.
SDS for Automotive uses the latest AI and machine learning (ML) technologies to enable human-like autonomy experiences that emulate professional drivers. This is achieved through a full, deep learning-based E2E architecture using the latest production-ready neural network architectures—powered by a scalable data engine that trains ML models on massive amounts of real-world and synthetic data, ensuring safe, precise, and reliable operation in diverse conditions. Applied Intuition's deep technical expertise in AI spans all areas of autonomy technology, from perception to planning and controls, with AI integrated throughout the entire stack.
SDS for Automotive includes a comprehensive L2++ feature set, with a pathway to L3 and L4. Supported features include remote intelligent parking, emergency braking (AEB), advanced urban driving, and highway pilot.
Applied Intuition's automotive autonomy solution includes hands-on support from experts in software, simulation, and vehicle systems, with experience at top OEMs and autonomy programs. With integration expertise proven in real-world deployments across industries including trucking, construction, and defense, we work closely with OEMs to adapt our stack to their platforms, supported by a global engineering team across the U.S., Asia, and Europe.
ADAS technology enhances road safety by using sensors, software, and real-time data to assist with tasks like braking, steering, and monitoring the environment. Features such as automatic emergency braking, lane keeping, and blind spot detection help reduce human error—the leading cause of accidents—and improve driver awareness and reaction time.
SDS for Trucking
An autonomous trucking software stack consists of multiple layers of software that manage various aspects of truck automation, including perception, decision-making, navigation, and vehicle control.
Autonomous trucking software enhances vehicle safety by using sensors, cameras, and algorithms to detect and respond to road conditions, obstacles, and traffic in real time, reducing the risk of accidents.
Critical technologies include machine learning, sensor fusion, real-time data processing, GPS for navigation, and robust communication systems to ensure seamless operation and safety.
Machine learning algorithms play a crucial role in processing vast amounts of sensor data to make real-time driving decisions, adapt to new environments, and improve safety protocols.
Using autonomous software allows for reduced labor costs, increased efficiency, enhanced safety, and the ability to operate around the clock, significantly boosting commercial operations.
It enhances road safety by reducing human error, maintaining consistent driving behavior, and using predictive analytics to avoid potential hazards.
SDS for Mining & Construction
An off-road autonomy stack integrates elements such as AI and ML-based algorithm modules, reference hardware architecture, reference software applications, and tooling for system integration and validation. It enables autonomous vehicles to navigate complex, unstructured terrains safely and efficiently, improving operational effectiveness and reducing human intervention.
Automotive original equipment manufacturers (OEMs) and operators of off-road vehicles benefit from an autonomy stack, which enhances autonomous capabilities, increases safety, and ensures robust and reliable development in challenging off-road environments.
The autonomy stack ensures safety through robust sensor fusion, precise localization, and advanced perception systems that support use cases ranging from human-operated vehicles to full autonomy. It supports comprehensive visualization interfaces for clear decision-making, enhancing operator trust and safety in complex off-road environments.
Yes, Applied Intuition's off-road autonomy stack is designed for flexibility and can be integrated seamlessly with existing vehicle systems. Its modular architecture allows for customization to meet specific operational requirements, enabling enhancements in autonomy without the need for extensive modifications to current setups, ensuring a smooth transition to advanced autonomous capabilities.
The off-road autonomy stack utilizes the latest AI and ML technologies. It enables sophisticated perception algorithms that accurately identify and classify obstacles and terrain variations. AI-driven decision-making processes optimize route planning and obstacle avoidance strategies in real time, ensuring efficient and safe navigation through unpredictable off-road environments.
The Applied Intuition team includes software, simulation, and automotive experts with experience from the top global automotive OEMs, software companies, and autonomy programs. We work closely with each of our customers to address their needs, offer integration support, and deliver flexible solutions with a white-box approach.