Live · The Road to Autonomy Indices
.RCI · Robotaxi Confidence Index 57.8 ▼ +0.1 .ADLCI · Autonomous Driving Licensing Confidence Index 43.3 ▲ +0.1 .ATCI · Autonomous Trucks Confidence Index 42.0 ▲ +0.1 .DCI · Delivery Bots Confidence Index 55.8 – 0.0 WaymoUS 84.0 ▼ -0.3 Baidu Apollo GoCN 82.0 ▲ +0.2 WeRideCN 74.7 ▼ -2.4 Pony.aiCN 67.3 ▼ -1.9 Starship TechnologiesEE 67.0 – 0.0 NeolixCN 66.4 – 0.0 XPengCN 63.4 ▲ +9.2 Serve RoboticsUS 63.2 ▼ -2.7 KodiakUS 59.0 ▲ +1.4 Applied IntuitionUS 55.4 – 0.0 TeslaUS 55.1 ▲ +2.4 AuroraUS 53.2 ▲ +3.8 MomentaCN 53.1 ▲ +0.8 CocoUS 52.6 ▲ +3.2 Cao Cao MobilityCN 50.1 ▲ +1.2 DeepRoute.aiCN 50.0 ▲ +2.6 Didi Autonomous DrivingCN 49.2 ▲ +1.8 MobileyeIL 45.0 ▼ -3.0 MeituanCN 42.9 – 0.0 DoorDash DotUS 42.1 ▼ -1.8 Bot AutoUS 41.6 ▼ -1.0 May MobilityUS 40.0 ▼ -0.6 AvrideUS 39.0 ▲ +0.1 ZooxUS 39.0 ▼ -5.0 WaabiCA 38.8 ▲ +5.1 Volvo Autonomous SolutionsSE 38.4 – 0.0 Avride PodUS 37.9 – 0.0 MotionalUS 35.7 ▲ +0.1 WayveGB 35.6 ▲ +4.6 MOIA AmericaDE 34.0 – 0.0 TorcUS 31.3 ▼ -1.4 Tensor AutoUS 27.3 ▼ -0.5 VerneHR 27.0 ▼ -0.8 AutobrainsIL 22.4 – 0.0 Stack AVUS 21.7 – 0.0 NuroUS 20.9 ▼ -3.7 Helm.aiUS 19.3 – 0.0 PlusAIUS 17.4 ▲ +0.3 HUMAINSA 5.1 ▲ +2.2 .RCI · Robotaxi Confidence Index 57.8 ▼ +0.1 .ADLCI · Autonomous Driving Licensing Confidence Index 43.3 ▲ +0.1 .ATCI · Autonomous Trucks Confidence Index 42.0 ▲ +0.1 .DCI · Delivery Bots Confidence Index 55.8 – 0.0 WaymoUS 84.0 ▼ -0.3 Baidu Apollo GoCN 82.0 ▲ +0.2 WeRideCN 74.7 ▼ -2.4 Pony.aiCN 67.3 ▼ -1.9 Starship TechnologiesEE 67.0 – 0.0 NeolixCN 66.4 – 0.0 XPengCN 63.4 ▲ +9.2 Serve RoboticsUS 63.2 ▼ -2.7 KodiakUS 59.0 ▲ +1.4 Applied IntuitionUS 55.4 – 0.0 TeslaUS 55.1 ▲ +2.4 AuroraUS 53.2 ▲ +3.8 MomentaCN 53.1 ▲ +0.8 CocoUS 52.6 ▲ +3.2 Cao Cao MobilityCN 50.1 ▲ +1.2 DeepRoute.aiCN 50.0 ▲ +2.6 Didi Autonomous DrivingCN 49.2 ▲ +1.8 MobileyeIL 45.0 ▼ -3.0 MeituanCN 42.9 – 0.0 DoorDash DotUS 42.1 ▼ -1.8 Bot AutoUS 41.6 ▼ -1.0 May MobilityUS 40.0 ▼ -0.6 AvrideUS 39.0 ▲ +0.1 ZooxUS 39.0 ▼ -5.0 WaabiCA 38.8 ▲ +5.1 Volvo Autonomous SolutionsSE 38.4 – 0.0 Avride PodUS 37.9 – 0.0 MotionalUS 35.7 ▲ +0.1 WayveGB 35.6 ▲ +4.6 MOIA AmericaDE 34.0 – 0.0 TorcUS 31.3 ▼ -1.4 Tensor AutoUS 27.3 ▼ -0.5 VerneHR 27.0 ▼ -0.8 AutobrainsIL 22.4 – 0.0 Stack AVUS 21.7 – 0.0 NuroUS 20.9 ▼ -3.7 Helm.aiUS 19.3 – 0.0 PlusAIUS 17.4 ▲ +0.3 HUMAINSA 5.1 ▲ +2.2
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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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