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.RCI · Robotaxi Confidence Index 50.8 ▲ +0.4 .ADLCI · Autonomous Driving Licensing Confidence Index 32.8 ▼ +0.4 .ATCI · Autonomous Trucks Confidence Index 40.2 ▼ +0.4 .DCI · Delivery Bots Confidence Index 53.1 ▼ +0.7 WaymoUS 79.6 ▲ +2.3 Baidu Apollo GoCN 78.0 ▼ -1.9 WeRideCN 68.6 ▲ +12.5 Starship TechnologiesEE 67.4 ▲ +2.8 Pony.aiCN 60.5 ▼ -0.8 NeolixCN 56.5 ▼ -4.7 Serve RoboticsUS 52.3 ▼ -9.3 AuroraUS 51.8 ▼ -2.0 KodiakUS 51.3 ▼ -9.3 CocoUS 49.6 ▼ -1.3 Cao Cao MobilityCN 47.0 ▲ +2.8 MeituanCN 45.4 ▲ +0.8 TeslaUS 44.4 ▲ +0.8 Didi Autonomous DrivingCN 42.9 ▼ -4.9 Applied IntuitionUS 41.5 ▼ -4.8 DoorDash DotUS 41.4 ▼ -0.8 MomentaCN 40.4 ▲ +2.2 Bot AutoUS 39.9 ▲ +1.5 Volvo Autonomous SolutionsSE 39.8 ▼ -4.1 ZooxUS 38.3 ▼ -1.4 MotionalUS 35.1 ▲ +4.2 Avride PodUS 34.8 ▲ +4.5 AvrideUS 33.9 ▲ +2.2 May MobilityUS 33.5 ▼ -0.6 XPengCN 32.3 ▼ -13.8 DeepRoute.aiCN 32.1 ▼ -1.5 WaabiCA 31.3 ▲ +2.4 TorcUS 30.7 ▼ -2.5 MobileyeIL 29.3 ▼ -4.4 WayveGB 27.3 ▼ -0.7 VerneHR 26.0 ▲ +4.1 MOIA AmericaDE 24.5 ▼ -0.1 AutobrainsIL 22.1 ▲ +0.5 Stack AVUS 21.3 ▲ +2.5 NuroUS 21.1 ▲ +2.1 PlusAIUS 19.8 ▲ +2.8 Tensor AutoUS 19.7 ▲ +4.1 Helm.aiUS 17.5 ▼ -0.5 HUMAINSA 2.1 – 0.0 .RCI · Robotaxi Confidence Index 50.8 ▲ +0.4 .ADLCI · Autonomous Driving Licensing Confidence Index 32.8 ▼ +0.4 .ATCI · Autonomous Trucks Confidence Index 40.2 ▼ +0.4 .DCI · Delivery Bots Confidence Index 53.1 ▼ +0.7 WaymoUS 79.6 ▲ +2.3 Baidu Apollo GoCN 78.0 ▼ -1.9 WeRideCN 68.6 ▲ +12.5 Starship TechnologiesEE 67.4 ▲ +2.8 Pony.aiCN 60.5 ▼ -0.8 NeolixCN 56.5 ▼ -4.7 Serve RoboticsUS 52.3 ▼ -9.3 AuroraUS 51.8 ▼ -2.0 KodiakUS 51.3 ▼ -9.3 CocoUS 49.6 ▼ -1.3 Cao Cao MobilityCN 47.0 ▲ +2.8 MeituanCN 45.4 ▲ +0.8 TeslaUS 44.4 ▲ +0.8 Didi Autonomous DrivingCN 42.9 ▼ -4.9 Applied IntuitionUS 41.5 ▼ -4.8 DoorDash DotUS 41.4 ▼ -0.8 MomentaCN 40.4 ▲ +2.2 Bot AutoUS 39.9 ▲ +1.5 Volvo Autonomous SolutionsSE 39.8 ▼ -4.1 ZooxUS 38.3 ▼ -1.4 MotionalUS 35.1 ▲ +4.2 Avride PodUS 34.8 ▲ +4.5 AvrideUS 33.9 ▲ +2.2 May MobilityUS 33.5 ▼ -0.6 XPengCN 32.3 ▼ -13.8 DeepRoute.aiCN 32.1 ▼ -1.5 WaabiCA 31.3 ▲ +2.4 TorcUS 30.7 ▼ -2.5 MobileyeIL 29.3 ▼ -4.4 WayveGB 27.3 ▼ -0.7 VerneHR 26.0 ▲ +4.1 MOIA AmericaDE 24.5 ▼ -0.1 AutobrainsIL 22.1 ▲ +0.5 Stack AVUS 21.3 ▲ +2.5 NuroUS 21.1 ▲ +2.1 PlusAIUS 19.8 ▲ +2.8 Tensor AutoUS 19.7 ▲ +4.1 Helm.aiUS 17.5 ▼ -0.5 HUMAINSA 2.1 – 0.0
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Merging LiDAR Performance with Radar Robustness

Merging LiDAR Performance with Radar Robustness

Matthew Carey, Co-Founder & CEO, Teradar joined Grayson Brulte on The Road to Autonomy podcast to discuss the company’s emergence from stealth with $150 million in funding and the creation of a brand-new category of terahertz (THz) sensors.

The operational backbone of Teradar’s strategy is a Terahertz Detection and Ranging (Rad-AR) approach that fills the gap between LiDAR and radar on the electromagnetic spectrum. By utilizing a modular architecture of Lego-like transmitter and receiver chips, the system provides the high-resolution point cloud typically associated with lidar while maintaining the all-weather robustness and velocity-sensing Doppler capabilities of radar. This solid-state design allows the sensor to be hidden behind vehicle bumpers or polymers, eliminating the need for bulky roof-mounted hardware.

In the field, Teradar is rigorously applying its technology to solve the weather casino problem, proving the system’s robustness in the heavy rain, snow, and dense fog of Boston. Unlike traditional vision or LiDAR systems that struggle with atmospheric particulates, Teradar’s longer wavelengths can bend around rain and dust, ensuring consistent performance in environments where humans or other sensors might fail.

Teradar’s Physical AI ecosystem also includes a defense-grade application that provides situational awareness in combat environments without being easily detected. The atmosphere effectively blocks the sensor’s signal beyond its intended range, allowing it to operate in dense traffic or military zones without jamming other sensors or revealing a vehicle’s position to hostile actors.

Looking ahead, Matt envisions a future where high-performance sensing reaches a mass-market inflection point by becoming affordable enough for every vehicle, from a Mercedes S-Class to a Ford Focus. By partnering with Tier 1 suppliers rather than vertically integrating, Teradar aims to scale to millions of units, fundamentally transforming the industry by delivering a sensor stack that costs hundreds, not thousands of dollars.

Episode Chapters

  • 00:00 Teradar Emerges from Stealth
  • 03:01 Limitations of Existing Sensor Technologies
  • 05:54 Introducing Terahertz Sensing
  • 08:00 Defense and Battlefield Applications
  • 11:11 Modular Sensor Architecture
  • 17:00 Early Development and Startup Challenges
  • 26:54 Why Teradar Chose Boston
  • 36:11 Autonomous Vehicles and Weather
  • 46:06 Scaling Teradar

The future is bright. The future is autonomous. The future is The Road to Autonomy.

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