iDAR stands for Intelligent Detection and Ranging
Current LiDAR sensors rely on an array of independent sensors that collectively produce a tremendous amount of data. This requires lengthy processing time and massive computing power to collect and assemble data sets by aligning, analyzing, correcting, down sampling, and translating them into actionable information that can be used to safely guide the vehicle.
In addition, these systems lack intelligence and gather information indiscriminately about the environment. They do not take into account how conditions evolve or know how to balance competing priorities, often responding poorly to complex or dangerous situations by assigning every pixel the same priority.
iDAR solves those challenges.
In its first instantiation, iDAR takes solid-state agile LiDAR, fuses it with a low-light HD camera, then integrates artificial intelligence to create a smart, software definable platform that enables faster, more accurate, and more reliable artificial perception.
AEye’s iDAR combines breakthrough innovations to solve critical challenges in perception and path planning.
The bottom line is this: Correctly implemented iDAR increases the speed of a car’s artificial perception by up to 10 times, while reducing power consumption 5 to 10 times. It does this by decreasing how much data is conveyed to the motion-planning system—in many cases, by more than 90 percent.
What Is iDAR?
iDAR is an artificial perception platform designed to adapt to new technologies and algorithms, continually evolving to minimize cost and maximize performance of the camera/LiDAR combination.
iDAR uses the world’s first solid-state agile LiDAR (Light Detection and Ranging).
AEye’s iDAR platform offers extremely fast scanning and automotive reliability in a small form factor that is designed to be mass produced at a fraction of the cost of existing sensors.
While AEye’s agile LiDAR delivers ranges up to 300 meters at high resolution, its ability to dynamically adapt its scanning patterns and to identify and focus on specific Regions of Interest (ROIs) within a single frame is what really gets perception engineers excited. Integrating embedded artificial intelligence enables the platform to learn and adapt as driving situations change.
Leveraging the unique capabilities of Dynamic Vixel data means AEye’s agile LiDAR can target and identify objects within a scene 10 to 20 times more accurately than LiDAR-only products—and deliver the information 10 times faster.
The result is greater reliability and longer range than existing LiDAR sensors along with greater safety and performance at lower cost.
Dynamic Vixels – integration of 2D camera and 3D agile LiDAR
AEye’s iDAR platform enables vehicles to see and perceive like people so self-driving cars can intelligently assess hazards and respond to changing conditions. An innovation at the core of this capability is Dynamic Vixels, a new sensor data type that combines pixels from 2D cameras with voxels from LiDAR.
This real-time integration of pixels and voxels means the data is handled more quickly, efficiently, and accurately at the sensor level, rather than in later processing. The resulting content empowers artificial intelligence to evaluate a scene using 2D, 3D, and 4D information to identify location, track objects, and deliver insights with less latency, bandwidth, and computer power.
In essence, using this new data type within iDAR’s architecture makes it possible to give self-driving cars better-than-human reflexes without the distraction factor that we humans are prone to. So, the autonomous vehicle with iDAR can more intelligently assess and respond to conditions in the environment, improving safety and reliability.
Artificial Intelligence and Software Definability
Embedded artificial intelligence enables iDAR to use thousands of existing and custom computer vision algorithms for insights that can be leveraged by path-planning software.
Current LiDAR-only solutions lack this intelligence and face severe limitations in their ability to respond to changing conditions.
Current LiDAR-only solutions simply collect as much data as possible without discretion and pass it to a central processor where 75 to 95 percent of it is discarded because it is redundant or useless. This creates a huge strain on interrogation times, bandwidth and processing, causing latency.
With agile targeting and intelligence in the data collection process, the iDAR platform collects and/or selects and analyzes only the data that matters—without missing anything.
This scalable, software definable approach allows the system to capture more intelligent information with less data, enabling faster, more accurate, and more reliable perception and path planning—key to the safe rollout of autonomous vehicles.
A Platform for Perception Innovation
The world’s first commercially available 2D/3D perception platform designed to run in the sensors of autonomous vehicles.
Basic perception can now be distributed to the edge of the sensor network, enabling the collection of data in real time, enhancing existing centralized perception software platforms by reducing latency, lowering costs, and securing functional safety.
Perception advancements will be made available through a software reference library, which includes the following features that will be resident in AEye’s 4Sight A (ADAS) and 4Sight M (Mobility) sensors:
- Detection: Identification of objects (e.g. cars, pedestrians, etc.) in the 3D point cloud and camera. The system accurately estimates their centroids, width, height and depth to generate 3D bounding boxes for the objects.
- Classification: Classifying the type of detected objects. This helps in further understanding the motion characteristics of those objects.
- Segmentation: Further classifying each point in the scene to identify specific objects those points belong to. This is especially important to accurately identify finer details, such as lane divider markings on the road.
- Tracking: Tracking objects through space and time. This helps keep track of objects that could intersect the vehicle’s path.
- Range/Orientation: Identifying where the object is relative to the vehicle, and how it’s oriented relative to the vehicle. This helps the vehicle contextualize the scene around it.
- True Velocity: Leveraging the benefits of agile LiDAR to capture the speed and direction of the object’s motion relative to the vehicle. This provides the foundation for motion forecasting.
- Lane Marking Detection: Detection and classification of every type of lane marking, ensuring safe vehicle navigation.
- Drivable Area: Detection of empty space in front of the vehicle until the next road obstacle (e.g. another vehicle, pedestrians etc.), as well as detection of the lane markings on either side of the vehicle or in the lane to its left or right.
- Motion Forecasting: Forecasting where the object will be at different times in the future. This helps the vehicle to assess the risk of collision and charter a safe course.
iDAR in Action
Living on the Edge
Every day, drivers face an incredible variety of situations and scenarios—terrain, roadway types, traffic conditions, weather conditions—for which autonomous vehicle technology needs to navigate both safely, and efficiently. These are edge cases, and they occur with surprising frequency. In order to achieve advanced levels of autonomy or breakthrough ADAS features, these edge cases must be addressed. But what are these common, real-world scenarios that are so difficult for conventional perception solutions to handle reliably? And how does AEye’s software definable iDAR platform successfully respond to these challenges, improving overall safety?
Explore the following Edge Cases to find out…
Anyone can say they value safety. At AEye, we put safety into motion.
AEye’s Edge Case Series demonstrates the necessity of a software definable perception platform to ensure the overall safety and efficiency of ADAS features and autonomous vehicles.
iDAR is smarter than LiDAR.