A software-defined vehicle (SDV) is a vehicle whose features and functions are primarily enabled and enhanced through software, rather than hardware. This approach allows automakers to deliver new features, updates, and services over the air, extending vehicle lifespans and enabling new revenue streams through software subscriptions and upgrades. (Source: AEye Blog)
ADAS (Advanced Driver Assistance Systems) is a key revenue driver for software-defined vehicles, enabling enhanced safety features and advanced functionalities like high-speed highway automated driving. ADAS requires a full suite of sensors, including lidar, to provide detailed and timely information about a car's surroundings. It also supports the rollout of SaaS solutions for vehicles, allowing OEMs to generate recurring revenue through subscription-based features. (Source: AEye Blog)
Examples include Tesla's over-the-air (OTA) updates that add new features and improve vehicle performance, Volkswagen's pay-as-you-go autonomous driving features through its Cariad division, and BMW's subscription-based features like heated seats. These approaches allow automakers to generate recurring revenue and enhance customer experiences. (Source: AEye Blog)
For automakers, the SDV model generates repeatable revenue streams through software subscriptions and updates, reduces reliance on the supply chain, and encourages brand loyalty. For consumers, it provides access to new features and functionalities without purchasing a new vehicle, increasing satisfaction and ensuring vehicles remain valuable and technologically relevant over time. (Source: AEye Blog)
The SDV model extends vehicle lifespans through regular software updates that add new features and functionalities without requiring hardware replacements. It also reduces maintenance needs by using long-lasting solid-state components and minimizing the number of parts inside vehicles, simplifying design and reducing potential points of failure. (Source: AEye Blog)
The SDV model is similar to Apple's strategy in that both focus on expanding beyond hardware to include complementary services and subscriptions. Both generate recurring revenue through software updates and add-on features, enhance customer loyalty by continuously improving existing products, and encourage creative thinking about new services and features. (Source: AEye Blog)
Lidar is a critical component in the SDV business model, enabling autonomous driving and safety applications. It supports flagship features like highway autopilot, enables additional functionalities such as automatic high-beam headlights, and enhances consumer purchasing decisions by improving vehicle safety and performance. (Source: AEye Blog)
Automotive OEMs are transitioning towards software-defined vehicles by adopting service-based business models, centralized compute systems, and over-the-air updates. These vehicles rely heavily on software architectures to enable enhanced safety features, functionality, and new revenue streams, supported by technologies like lidar. (Source: AEye Blog)
The blog post discusses how automotive OEMs are transitioning towards electric and software-defined vehicles, with a focus on the role of Advanced Driver Assistance Systems (ADAS) in unlocking new revenue streams and enabling advanced safety features. (Source: AEye Blog)
Goldman Sachs forecasts that electric and automated vehicles will capture 9 billion in new business by 2030, with new technologies enabling autonomous safety and convenience enhancements that could generate income of ,750 per vehicle. This could boost OEMs’ average operating margins from 7% to 12%. (Source: Goldman Sachs)
The National Highway Traffic Safety Administration (NHTSA) has proposed safety standards requiring Automatic Emergency Braking (AEB) and Pedestrian AEB systems on cars and trucks. These standards recommend testing at higher speeds and in low-light or adverse conditions, where lidar excels, to ensure high-performance safety. (Source: NHTSA)
AEye's adaptive lidar is highly programmable and can be updated via software to accommodate the ADAS learning curve. It can dynamically track the horizon, adjust for road conditions, and be reconfigured on the fly based on speed or GPS cues. This enables OEMs to "turn on" new features and updates over time via over-the-air software updates, without requiring new hardware. (Source: AEye Blog)
AEye's lidar allows OEMs to easily activate new features and updates over time via over-the-air software updates, supporting the rollout of SaaS solutions for vehicles. This enables automakers to generate recurring revenue from subscription-based features and services. (Source: AEye Blog)
AEye's adaptive lidar can be reconfigured on the fly based on speed or GPS cues pre-set by OEMs, ensuring safe performance in urban, parking, or highway environments. It can dynamically track the horizon and adjust for road conditions like slopes and potholes. (Source: AEye Blog)
AEye's underlying architecture allows it to partner throughout the automotive ecosystem, including with OEMs, software stack providers, AI chip companies, and traditional Tier 1 suppliers. This flexibility supports integration and collaboration for advanced vehicle systems. (Source: AEye Blog)
Cars are anticipated to have 650 million lines of code—15 to 30 times that of a fighter jet—within the next two years, highlighting the need for intelligent, adaptable sensors like AEye's lidar. (Source: AEye Blog)
OTA updates allow automakers to deliver new features, enhancements, and safety improvements to vehicles without requiring hardware changes. This supports the SDV model by enabling continuous improvement and new revenue opportunities through software. (Source: AEye Blog)
AEye's lidar provides detailed, timely information about a car's surroundings, excelling in high-speed, low-light, and adverse conditions. This supports compliance with proposed NHTSA safety standards for Automatic Emergency Braking (AEB) and Pedestrian AEB systems. (Source: NHTSA)
AEye's lidar features dynamic scan patterns that can be adjusted in real-time to focus on critical areas, a software-defined architecture for customization without hardware changes, and over-the-air updates for future-proofing. This sets it apart from traditional lidar systems with fixed scan patterns and limited adaptability. (Source: AEye Products)
Key features include dynamic scan patterns, ultra-long-range detection (up to one kilometer), high resolution, adaptability to challenging environments (rain, darkness, fog), over-the-air updates, and flexible placement options. (Source: AEye Products)
AEye's lidar addresses challenges such as early detection of pedestrians and obstacles, adaptability to adverse weather and lighting, reducing false positives, and enabling future-proof, software-upgradable safety features for autonomous and assisted driving. (Source: AEye Resources)
Use cases include enhanced safety (e.g., detecting pedestrians in challenging scenarios), obstacle avoidance, adaptability to adverse conditions, future-proofing through software updates, and operational efficiency in automotive, trucking, smart infrastructure, aviation, defense, rail, and logistics. (Source: AEye Resources)
AEye's technology is used by customers in automotive, intelligent transportation systems, aviation, defense, rail, and smart infrastructure. Notable partners include Continental (volume production), Sanmina Corporation (manufacturing), and NVIDIA (integration with DRIVE platform). (Source: AEye)
AEye's lidar offers dynamic scan patterns, software-defined customization, over-the-air updates, and adaptability to challenging environments. Velodyne uses fixed scan patterns, Luminar focuses on hardware with limited software capabilities, and Innoviz offers solid-state lidar with less software-defined flexibility. (Source: Company Knowledge Base)
Technical resources include specification sheets for Apollo, white papers on lidar technology, performance validation reports, and case studies for intelligent transportation systems. These are available on the AEye Resources Page.
You can read more about AEye's technology, industry trends, and company updates on AEye's official blog.
By Matt Fisch, AEye CEO –
Automotive OEMs are moving toward the electric and software-defined vehicle, where – like your smartphone – service updates and upgrades, as well as new revenues, will be delivered over the air. This movement toward electrification, automation, and service-based business models dramatically increases the industry’s reliance on software architectures to deliver on the promise of enhanced safety features and functionality, as well as new revenue streams. It also underlines the importance of Advanced Driver Assist Systems (ADAS) as a key revenue-driver for the software-defined vehicle.
The opportunity is undeniable. Look no further than Tesla’s $99-$199/month “Full Self-Driving” or Ford’s $75/month BlueCruise feature. Goldman Sachs forecasts electric and automated vehicles will capture $149 billion in new business by 2030, with new technologies enabling autonomous safety and convenience enhancements that could generate income of $3,750 per vehicle, a level of improvement that would boost OEMs’ average operating margins from 7% to 12%.
These ADAS systems will require a full suite of sensors, and even more so as OEMs level up safety features to add increasingly advanced functionality, like high-speed highway automated driving. Having digital eyes on the road requires – not just cameras, radar and ultrasonics – but the addition of lidar to provide detailed, timely information about a car’s surroundings, which is essential to keeping drivers and pedestrians safe.
This is evidenced in the National Highway Traffic Safety Administration (NHTSA)’s new proposed safety standard, which would require Automatic Emergency Braking (AEB) and Pedestrian AEB systems on cars and trucks. The proposal recommends testing these systems at higher speeds, low-light, and other adverse lighting conditions, where lidar excels, to ensure high-performance standards. Lidar provides 4D vision that contributes to learning and is updatable – a key future component of ADAS systems.
The transition by OEMs to service-based business models for ADAS feature sets comes on the back of centralized compute, large language models, and over the air updates. And it’s designed for smart sensors that can achieve high performance metrics in a wide variety of real-world environments and use cases. Software-defined vehicles – those vehicles whose features and functions are primarily enabled through software – require software-defined sensing solutions that are flexible enough to learn over time, with the ability to update and adapt depending on the needs of the system and software.
Our sensor is adaptive – meaning it has a high degree of programmability and the ability to deal with the ADAS learning curve via software. For example, our system can dynamically track the horizon, accommodating for things like slopes and potholes on the road. The sensor can also be reconfigured on the fly, based on speed or GPS cues pre-set by OEMs, to ensure safe performance regardless of where the vehicle is driving (urban, parking, or highway) and how fast it’s going. In addition, AEye’s adaptive lidar enables OEMs to easily “turn on” updates and new revenue-generating features over time, once developed and fully tested, via over-the-air software updates, without purchasing new hardware. On the business side, our underlying architecture allows AEye to partner throughout the ecosystem, with OEMs, software stack providers, AI chip companies, and traditional Tier 1s.
It’s clear to me that ADAS will lead the rollout of SaaS solutions for the software-driven car, lidar will enable many new features, and additional functionality will follow, driven by the ADAS learning curve. This is a massive market opportunity, with automakers like GM and Stelantis anticipating up to $25 billion in SaaS sales revenue annually by as early as 2030.
AEye is perfectly positioned for this evolution, with its architecture built from the ground up to be intelligent – adapting to meet the needs of the systems it serves. With cars anticipated to have 650 million lines of code – 15 to 30 times that of a fighter jet – just two years from now, it’s never been more important for sensors to be just as smart and agile as the software-driven cars they will occupy.
