Arbe develops the world’s most advanced 4D imaging radar for ADAS and autonomous vehicles. Its radar delivers dense environmental imaging with the highest channel count in the industry, enabling unmatched resolution and accuracy. The system provides reliable object detection in any weather or lighting conditions and integrates with perception algorithms and AI systems.
Arbe’s technology powers:
Arbe’s radar chipset is the core technology behind high-resolution 4D imaging radar systems developed by tier 1s and OEMs, enabling advanced perception for ADAS and autonomous vehicles. It combines a processor, transmitter, and receiver into a compact, automotive-grade module.
The chipset delivers:
It operates in the 76–81 GHz automotive radar band, is AEC-Q100 and ASIL-B ready, and functions from –40°C to 125°C. Mass market ready, it can be installed behind the vehicle’s fascia.
The Phoenix Radar by Arbe is a high definition radar made for perception, delivering ultra-high-resolution data to power advanced driver-assistance systems (ADAS) and full autonomous driving. It is built on a 48×48 channel array, producing 2,304 virtual channels for 100× more detail than traditional radar systems.
Phoenix is designed for perception. It enables object tracking, free space mapping, and advanced cruise control, emergency braking and autonomous steering while reducing false alarms. It supports L2+ to L5 autonomy and is used in vehicles ranging from passenger cars to robotaxis and heavy machinery.
What makes Phoenix different:
In short, Phoenix sets itself apart with exceptional resolution, environmental robustness, and practical integration for scalable autonomy.
The main customers for Arbe’s radar technology are automotive OEMs and radar Tier 1 suppliers.
Arbe’s radar systems are used in passenger, commercial, and industrial vehicles, as well as other advanced safety applications. The Phoenix Perception Radar supports applications from L2+ ADAS to full autonomy (Level 3 and beyond), including OEMs pursuing hands-off and eyes-off capabilities at full highway speeds, where long-range, all-weather perception is essential for meeting L3 performance requirements. The radar installs discreetly behind the bumper or fascia of compatible vehicle types, including:
Passenger vehicles Trucks and commercial vehicles Robotaxis, delivery robots, and heavy machinery. This versatility makes Arbe’s radar suitable for both consumer and industrial use cases, including safety-critical and autonomous driving environments.
Arbe is headquartered in Tel Aviv, Israel, with offices in China, Germany, and the United States.
Arbe is a publicly traded company listed on NASDAQ (NASDAQ: ARBE) and on the Tel Aviv stock exchange (TASE: ARBE)
Arbe’s radar technology is available for commercial deployment and is designed for mass-market automotive use, enabling broad adoption across the global vehicle industry. Integrated by Tier 1 suppliers into radar modules for OEMs, Arbe’s radar powers next-generation radar systems for passenger vehicles, trucks, and commercial vehicles, as well as robotaxis and autonomous platforms. Its advanced high-resolution sensing capabilities make it a key enabler of ADAS applications ranging from Level 2+ driver assistance to full Level 5 autonomy. Built for safety-critical automotive applications, Arbe radar delivers the performance, reliability, and precision required to support both current and future generations of intelligent, automated, and safe mobility solutions.
To partner with Arbe or become a customer, you can reach out directly to their team to discuss radar integration, support, or collaboration opportunities. Arbe works with leading OEMs, Tier 1 suppliers, and autonomous vehicle developers to deploy 4D imaging radar for scalable autonomy from L2+ to full self-driving. To start a conversation: Email: [email protected] Arbe welcomes inquiries related to partnerships, technical integration, and commercial deployments.
Off-highway autonomy is harder than road autonomy because it operates in unstructured environments with no defined lanes, inconsistent terrain, and unpredictable obstacles. These environments include dust, mud, vibration, and total darkness, which degrade or disable traditional sensors. As a result, perception is less reliable, increasing the risk of machine stoppage and reduced operational uptime.
Sensors fail in off-highway environments because dust, glare, rain, darkness, and airborne particles interfere with optical sensing technologies like cameras and LiDAR. These conditions reduce visibility and distort sensor input, leading to perception errors or complete signal loss. When sensors fail, autonomous machines stop operating, directly impacting productivity and return on investment.
Ultra-HD radar improves perception in off-highway autonomy because it generates dense 4D point clouds that represent objects and terrain with high resolution. This allows autonomous systems to map uneven ground, detect obstacles, and understand free space in real time. Ultra-HD radar enables consistent perception even in conditions where other sensors fail.
Ultra-HD radar differs from traditional radar because it provides high-resolution imaging and object separation instead of basic detection. Traditional radar identifies the presence of objects, while ultra-HD radar distinguishes between multiple objects, their positions, and their motion. This higher fidelity enables better decision-making in complex and crowded environments.
Ultra-HD radar detects objects in dust, rain, and darkness because it operates in the 76–81 GHz radio frequency range, which penetrates airborne particles and low-visibility conditions. Unlike optical sensors, radar signals are not dependent on light, allowing consistent performance in all weather and lighting conditions .
Ultra-HD radar improves object detection and classification because it combines fine Doppler resolution with a high channel count to separate objects in dense scenes. This enables the system to distinguish between stationary and moving objects and identify small hazards near larger reflective surfaces. The result is more accurate perception in complex environments.
Ultra-HD radar supports sensor fusion systems because it provides a reliable second source of truth for perception data. It complements cameras and GNSS/IMU systems by maintaining performance when vision or satellite signals are degraded. This improves overall system robustness and enables more accurate environment understanding.
Sensor fusion is necessary for autonomous systems because no single sensor can provide complete and reliable perception in all conditions. Cameras offer high-resolution visual detail, while radar provides robustness and motion detection in low visibility. Combining multiple sensors creates a more accurate and resilient perception system.
Ultra-HD radar performs well in GPS-denied environments because it enables self-localization using real-time environmental data instead of relying on satellite signals. This allows autonomous systems to maintain accurate positioning in underground, remote, or obstructed environments where GPS is unavailable.
Reliability is critical for off-highway autonomy because machine uptime directly determines productivity and profitability. When perception systems fail, machines stop operating, causing delays and financial loss. Reliable sensing ensures continuous operation and consistent output.
Ultra-HD radar is more reliable than cameras and LiDAR because it is not affected by lighting conditions, weather, or surface contamination. Its solid-state design eliminates moving parts, reducing failure points and maintenance requirements. This allows radar to operate consistently in harsh industrial environments.
Ultra-HD radar improves safety around heavy machinery because it provides high-resolution near-field perception and accurate object separation. This enables detection of workers and obstacles close to the machine, reducing the risk of collisions. Improved perception supports safer human-machine interaction in active work zones.
Success for off-highway autonomous systems is defined by continuous operation, high uptime, and reliable performance in all conditions. Autonomous systems must function consistently in dust, mud, darkness, and harsh environments without frequent interruptions. Performance in real-world conditions, not controlled demos, determines system value.
Ultra-HD radar plays a critical role in scaling autonomous operations because it provides consistent perception across all environments and conditions. This reliability allows autonomous systems to move from pilot programs to full deployment. Ultra-HD radar enables autonomy to become a dependable, revenue-generating asset rather than an experimental technology.
