Embark's Autonomous Trucks Overcome Weather Challenges with Advanced On-Road Testing
Technology Category
- Robots - Autonomous Guided Vehicles (AGV)
- Sensors - Autonomous Driving Sensors
Applicable Industries
- Automotive
- Transportation
Applicable Functions
- Logistics & Transportation
- Quality Assurance
Use Cases
- Autonomous Transport Systems
- Experimentation Automation
Services
- Testing & Certification
About The Customer
Embark Trucks is the longest-operating self-driving truck program in the United States, having been a forerunner in autonomous vehicle technology for trucks since 2016. Their software powers safe, commercially viable autonomous long-haul trucks, trusted by investors, Fortune 500 shippers, and some of the nation’s largest carriers. Embark's autonomous trucks use sensing technologies such as LiDAR, radar, and optical cameras to collect visual data from the surrounding environment, which is then combined with maps and algorithms to make decisions while on the road.
The Challenge
Road logistics operators in the United States often face revenue loss due to the inability to operate in severe weather conditions, particularly in Northern states. Autonomous trucks, such as those developed by Embark Trucks, have the potential to transform the industry, but they face their own challenges. Autonomous vehicles (AVs) rely on sensing technologies like LiDAR, radar, and optical cameras to collect visual data from their surroundings, which is then combined with maps and algorithms to make decisions. However, even the most advanced sensing technology can struggle with accurate detection and interpretation of road conditions in adverse weather. Embark needed not only on-road testing but also accurate and complete historical weather datasets to fully understand the implications of such conditions on its self-driving solution.
The Solution
Embark turned to an AerisWeather Flex subscription to obtain granular weather data, pulled on location-based intervals frequently throughout the day. This allowed Embark to develop a comprehensive weather model comprised of over eight billion historical weather data points dating back over 10 years for major routes across the United States. The model increased Embark's confidence in its autonomous trucks' ability to assess and react to the impact of snow at a lane level. The data was also combined with highway requirements, such as the requirement for highways to be cleared within three hours of snow stopping. This enabled Embark to estimate when highways would be cleared at any given location. Trucks were test-driven in snowy conditions and data from the truck’s sensors was cross-referenced with AerisWeather’s interpolated conditions, allowing Embark to determine the possible business implications of snowy conditions.
Operational Impact
Quantitative Benefit
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