Torc Takes Texas: Bringing Autonomous Trucking to Ft. Worth, Austin, and San Antonio

Torc Takes Texas: Bringing Autonomous Trucking to Ft. Worth, Austin, and San Antonio

If first responders and government officials can’t make it to Torc, Torc will bring autonomous trucking technology to them, especially in Texas!

Torc took to the Texas highways May 6 – 9, 2024, visiting locations in Ft. Worth, Austin, and San Antonio. With a traveling, custom-wrapped event trailer as our location on wheels and our ADS-ready Freightliner Cascadia, Torc hosted over two hundred first responders, transportation experts, and the public safety community. The week was filled with amazing conversations about autonomous trucking and Torc’s First Responder Interaction guides, while sharing Torc’s commitment to safety and innovation.

Torc’s First Responder Guide provides information on how first responders can safely interact with our trucks. More information about our First Responder Guide can be found here.

The tour was an important opportunity to share Torc’s vision and provide attendees with a first-hand experience with our innovative long-haul trucking technologies. Guests were able to climb into the truck cab, view the controls, and learn about the sensors, cameras, and software that encompass our autonomous driving system. Importantly, it provided education about the future ahead, answering questions, offering facts, and explaining how autonomy will help shape freight logistics.

Check out the video below for a recap of the event and more information from Michelle Chaka, Torc’s Senior VP of Safety and Regulatory.

In Case You Missed It:

Be sure and check out Michelle Chaka’s webinar on Safely Delivering Autonomous Trucking Solutions

 


Torc to Present Nine Papers at CVPR 2024

Torc to Present Nine Papers at CVPR 2024

Driving Autonomous AI with Torc’s Head of Intelligence

This summer, Torc’s Head of Artificial Intelligence, Felix Heide, and his team of both Princeton University and Torc colleagues will present an extraordinary total of nine papers at the IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR). Featuring information on geometry reconstruction, generative method, learning techniques, novel datasets and more, Felix and his team will exhibit their core competencies in researching and creating AI tools and techniques for self-driving artificial intelligence and beyond.

Torc at IEEE/CVF CVPR Conference

As one of the primary events in the field of computer vision, CVPR brings leading researchers, industry professionals, academics, and more, to a weeklong conversation about the latest findings in the world of computer vision and pattern recognition. With its main exhibition floor, presentations, and expert attendees, CVPR offers all levels of computer science professional with an opportunity to share their cutting-edge research and collaborate within the community. The IEEE/CVF CVPR Conference serves as the perfect backdrop for the innovative research Torc is spearheading.

The nine papers at CVPR 2024 cover a wide range of topics, each offering unique insights and contributions to the field. From advancements in neural rendering to new types of lidar, Felix, joined by his Princeton and Torc teams, has developed comprehensive content that reflects the depth and breadth of the industry’s expertise. Their work showcases the latest developments in computer vision and pattern recognition and indicates the ongoing progress of autonomous trucking technology.

Innovating Freight with AI

The significance of this research extends far beyond the confines of academic discourse. By pushing the boundaries of AI and machine learning, this work has the potential to revolutionize the autonomous driving industry and reshape the way we interact with technology. At Torc, these contributions help us pave the way for safer, more efficient freight systems that will benefit fleet professionals, everyday consumers, and all highway users.

As we move forward, AI research points us towards exciting new possibilities for collaboration and innovation. As academia, industry, and other research organizations come together to tackle the challenges of tomorrow, conferences like CVPR play a crucial role in fostering dialogue and driving progress. By sharing their insights and expertise, researchers like Felix and his team can shape the future of AI and propel us towards a world where driverless trucks enhance our lives in ways we’ve only begun to imagine.

These contributions not only highlight the cutting-edge research happening at Torc Robotics but also underscore the validity and potential of autonomous technology. As we continue to innovate our automated truck technology, our team’s contributions propel us to drive the future of freight in new and innovative ways. For our team, self-driving AI isn’t just a future concept, but a tangible reality that will make our world safer, faster, and stronger.

 

About Felix Heide, Torc’s Head of AI

Felix Heide is an industry leader in both artificial intelligence and autonomous technology. His journey to becoming an expert in general artificial intelligence, self-driving AI, and machine learning began long before his time at Torc, ranging from his first internship with NVIDIA Research in 2013 to his current role leading autonomous trucking development.

Felix is an Assistant Professor at Princeton University, Head of AI at Torc Robotics, and founder of the self-driving vehicle startup Algolux (now part of Torc). He is researching the theory and application of computational imaging and computer vision systems. Exploring imaging, vision, and display systems end-to-end, Felix’s work lies at the intersection of artificial intelligence, computer graphics, and computer vision.

He received his Ph.D. from the University of British Columbia, his undergraduate degree from the University of Siegen, and was a postdoc at Stanford University. His doctoral dissertation won the Alain Fournier Dissertation Award and the SIGGRAPH outstanding doctoral dissertation award. He won the NSF CAREER Award 2021 and the Sony Young Faculty Award 2021. He was named a Packard Fellow in 2022 and a Sloan Research Fellow in 2023. Felix was named SIGGRAPH New Significant Researcher in 2023.

 

 


This article is part one of three articles about Torc’s presence at CVPR this year. Look for more news about specific paper information, and the CVPR event in June.

Lane-Keeping in Self-Driving Trucks: Precision and Trust

Lane-Keeping in Self-Driving Trucks: Precision and Trust

Across all the features that self-driving technology has to offer, we might think of lane keeping as one of the most basic features possible. However, lane keeping is a complex behavior that relies on multiple components, sensors, and procedures to complete safe driving behaviors. In self-driving technology, precision is everything, making lane keeping a foundational necessity that underscores the safe and efficient operation of robotic trucks.

What is lane keeping?

Lane keeping is a critical driving functionality that ensures a vehicle stays within its designated lane on the road. Many newer consumer cars and commercial semi-trucks have some form of autonomous lane-keeping system programmed in, albeit there are several nuances and differences between types of lane-keeping systems, such as:

Lane Keep Assist (LKA)

Lane Keep Assist is a feature that can be toggled on and off on most vehicles. It typically works via camera, allowing the LKA to “see” the lane lines and nudge your vehicle within the lane lines when it begins to drift. However, because it’s camera-based, this feature may struggle to perform in muddy, snowy, or especially rainy conditions.

Lane Keep Assist is sometimes confused with Lane Departure Warning, which alerts drivers via haptic feedback, audible alerts, and sometimes indicator lights, if they’ve begun drifting out of the lane. Unlike other forms of lane-keeping systems, lane departure warning won’t correct the vehicle’s path. Instead, its job is to inform the driver that the vehicle is exiting the lane.

Lane Centering Assist (LCA)

Lane centering, sometimes called autosteer, takes LKA a step further. This feature is typically part of a vehicle’s adaptive cruise control, wherein a vehicle performs most highway behaviors itself while under human supervision. Lane centering is an active technology that keeps a vehicle in the center of its lane and can typically be turned on and off.

Today, many Class 8 trucks come equipped with various forms of Lane Keep Assist, Lane Centering Assist, and Adaptive Cruise Control. Aside from making drives safer for truck drivers and other highway users alike, these features can adjust throttle inputs and gear ratios for more efficient driving behaviors. By utilizing these features, drivers can optimize the amount of fuel their equipment consumes, reducing costs across the board.

Why is lane keeping important?

Lane keeping plays a foundational role in ensuring the safe and efficient operation of any vehicle, including our autonomous trucks. By enabling our robotic trucks to steadfastly maintain their designated lanes, we’re not only addressing a core competency in highway navigation but proving that our technology can be a safe foundation for a self-driving system.

Lane-keeping is important because of its impact on safety, but there are a few other reasons why we focus on this behavior as one of the most integral in safe-driving technology.

lane keeping

 

Traffic Flow and Predictability

When vehicles stay within their designated lanes, it reduces the likelihood of swerving and abrupt lane changes. When we humans learn to drive, we learn that being a predictable driver means being a safe driver.

Our autonomous technology is no different. In order to reduce the likelihood of traffic accidents and promote smoother traffic flow, we must ensure that other drivers are able to anticipate what our self-driving truck is going to do at all times. Whether that means keeping a consistent, steady pace within one lane or using an indicator light to shift lanes, predictability should be at the forefront of all lane-keeping behaviors.

Efficiency

By staying within the given lane, vehicles maximize the use of available road space, allowing more efficient traffic flow. During peak hours, when space is at a premium, this can reduce delays, avoid bottlenecks, and make it easier for vehicles to travel at a consistent speed throughout their journey.

How do self-driving cars and trucks stay in their lanes?

Self-driving trucks and self-driving cars stay in their lanes via cameras, Global Navigation Satellite Systems, LiDAR (or Light Detection and Ranging sensors), and more. Thanks to the work of autonomous driving engineers, our self-driving semi-trucks’ perception suite can recognize lane markers, interpret them correctly, and communicate this information to the rest of the system. From there, the autonomous driving system can utilize the information to maintain a set speed and keep watch on the distance between it and the vehicle in front of it.

There’s a common misconception that driverless cars and driverless trucks rely on lane markers alone to make sense of the path before them. While this used to be the case for very early self-driving cars, our autonomous abilities have advanced to grand new heights. Today, lane markers (and the cameras that “see” them) are just one piece of the puzzle.

Self-driving vehicles also utilize radar, which is sometimes found in Lane Keep Assist programs that we have in our day-to-day cars. Radar adds an additional safety component to lane navigation. Using radio waves to detect objects like other cars and traffic cones, radar helps paint the picture of what the driving environment looks like. In the same vein, mapping allows a self-driving vehicle to utilize historical information about the road to navigate in conjunction with the other tools in its toolbox. These two tools work with lane marker detection to assess the environment, calculate the safest possible behavior, and execute that behavior.

Lane Keeping and Robotic Trucks

As proponents of safe and sustainable self-driving practices, our autonomous driving system keeps in line with regulatory and industry best practices throughout all lane keeping behaviors. Aside from its impact on safety, proper lane keeping allows us to operate predictably to drivers on the road around us, prove our product’s viability, and promote a safe self-driving future.

As we forge ahead with our driverless trucking development, we will continue to innovate, collaborate, and lead the way in advancing our autonomous driving system. Through ongoing research, development, and collaboration with our stakeholders and partners, we will further enhance our lane-keeping capabilities to meet the evolving needs and expectations of the industry and the public. Together, we’re driving the future of freight.

 

 

Navigating the Road Ahead: Torc Robotics’ Self-Driving Truck Validation Journey

Navigating the Road Ahead: Torc Robotics’ Self-Driving Truck Validation Journey

Our Validation Strategy for Self-Driving Technology

At Torc Robotics, we’re at the forefront of self-driving truck technology. Our pursuit of innovation is underpinned by a comprehensive validation strategy that seeks to prove the feasibility of our self-driving truck product. Today, we’re diving into our validation approach, exploring the various forms of proof we employ, the criteria for achieving true Level 4 readiness, and the multi-pronged validation strategy that drives our groundbreaking work. 

Exploring the Self-Driving Challenge 

 Our validation strategy is supported by three core pillars: problem definition, current references, and proof. 

Understanding the Problem 

At the heart of Torc’s validation strategy is a clear definition of the self-driving challenge we’re addressing. By precisely outlining the complexities and intricacies of self-driving trucks, we lay the groundwork for our validation efforts. 

Understanding the problem begins with problem completeness. The operating domain is defined prior, with manageable parameters and modellable relationships. IFTDs, or In-Vehicle Fallback Test Drivers, provide source data of an ideal truck driver, allowing us to provide driving behaviors that correlate with a non-robotic driver’s ability. 

Our on-the-field teams act as a solid reference model for many aspects of our self-driving technology, including our validation strategy.

Reference Models

We rely on a number of reference models to understand the whole problem, including In-Vehicle Fallback Test Drivers (IFTDs), laws, voice of the customer, and more.  

In the case of our IFTDs, these professionals act as an integral piece of our validation process. These highly trained individuals are CDL-holding drivers with years of experience driving for logistics leaders across the United States; their driving behaviors are ideal resources for robotic truck behavior, giving us an effective reference point throughout software development. 

Proof: Rigorous Testing and Pushing Boundaries 

Our commitment to creating a safe, scalable self-driving truck extends beyond confirming functionality; we deliberately attempt to break our technology to reveal potential vulnerabilities. We employ various forms of proof: 

  • Direct Proof Based on Requirements. Data collected from test runs with our in-house semi-trucks forms the basis for formal testing. This includes techniques like black box testing and ad-hoc testing to comprehensively address anticipated challenges. 
  • Proof by Exhaustion. We subject our system to an exhaustive range of scenarios, leveraging simulations to expand testing without resource constraints. 
  • Proof by Contradiction. We intentionally introduce incorrect data to test the system’s adaptability. For instance, we might challenge the system with non-moving objects mimicking high-speed movement, feed two sensors different datasets, or otherwise attempt to “confuse” the autonomous driving system. 
  • Proof by Random. Our technology’s versatility is tested by placing it in unfamiliar environments, evaluating its ability to handle unforeseen scenarios. By baking randomness into our testing, we can ensure that we’re not just testing for known requirements and corner cases but for broader purposes. This way, there’s less chance that an easy case may trip up our design. 
  • Adversarial Testing. We provide our systems with input that is deliberately malicious and/or harmful. This is another form of “breaking” our system; it improves our technology by exposing failure points, allowing us to identify potential safeguards and mitigate risks. 

The five proof forms serve to prove that the technology is robust. If the system can overcome random variables, exhaustion, and contradiction to a reasonable degree, its robustness and adaptability will be validated, affirming its readiness for real-world challenges. Our ability to define the problem and our strategy to validate the desired behavior gives us the confidence that a solution exists. 

Our Multi-Faceted Validation Strategy 

Our validation approach embraces a multi-faceted strategy, driven by multiple aspects: 

  • Requirement Driven. Our validation efforts are guided by specific requirements that align with the intended functionality of our self-driving truck. We design for the known variables and the known unknown variables.  
  • Design Driven. We systematically validate our technology’s design to ensure alignment with Formal and Mathematical methods, enabled by MBSE, and validate that the system design is confirmed by the implemented system.  
  • Scenario Driven. Our technology is tested across a spectrum of real-world scenarios, ranging from routine to novel situations. We carefully define our system boundaries to minimize the unknown unsafe. 
  • Data Driven. Empirical evidence from real-world mileage, test runs, simulations, and controlled environments provides a factual basis for assessing our technology’s performance. This also allows us to expose new unknowns, validate assumptions that we’ve already made, and ensure that our requirements are as complete as possible.   

Driving the Future of Freight: Validation 

Torc Robotics’ validation strategy reflects a comprehensive approach to tackling the challenges of self-driving truck technology. By meticulously defining problems, embracing diverse proof techniques, and adhering to a multi-faceted validation strategy, we are propelling the industry towards true Level 4 readiness. Anchored in safety management and engineering rigor, Torc Robotics is not only shaping the trajectory of self-driving trucks but also setting a precedent for responsible and robust autonomous vehicle development. 

imaginAviation 2024 Panel: How Ideas Become Innovation

imaginAviation 2024 Panel: How Ideas Become Innovation

Innovation. Resistance. Transformation. Collaboration. These ideas paved the way for a panel discussion at imaginAviation 2024, featuring guest John Marinaro, Torc’s Vice President of Fleet Operations, along with host Dr. John A. Cavolowsky, NASA’s Director, Transformative Aeronautics Concepts Program (TACP), and guest Sheilla Torres-Nieves, Associate Professor, Fluid Dynamics and Turbulence at the University of Puerto Rico Mayaguez.

Among the more notable topics were resistance to innovation, transformational innovation, and inspiration for innovation.

Resistance to Innovation

When asked to provide examples of how to overcome an unwillingness to adopt innovation or accept change, Marinaro recounted a statement from the Columbia Accident Investigation board that “NASA Safety wasn’t as credible or competent as it should be.” He then explained: “I spent the rest of my career engineering that out of ever being said in an accident investigation again.” As he led innovation of a safety training program, he encountered resistance from some of the senior SMEs that training could be delivered online using revolutionary lecture-capture technology. However, thanks to beta testing, the program had 250 graduates on day one of the safety training’s deployment and proved a successful innovation.

At Torc, one of the primary challenges is resistance to the idea of self-driving vehicles replacing truck drivers. However, a shortage of drivers at the tune of 60,000, Marinaro explained, is disrupting the supply chain and resulting economics. Torc is looking to fill that gap. Marinaro indicated that Torc’s goal is to create safer conditions through technology that produces real-time reactions through awareness of a 360-degree environment, coupled with the reality that the truck doesn’t “get tired.” He concludes, “At the end day, we’re not going to replace the drivers. We’re just gonna augment them and make it safer.”

Transformational Innovation

When Dr. Cavolowsky posed the question of how we apply transformational innovation, how we get there and what kind of innovation we need to bring, Assistant Professor Torres-Nieves answered, “When we hear transformational, we think about changing the way we live drastically…changing culturally…changing from the fundamentals.” Torres-Nieves mentioned the “Change the World” talent competition offered at her university that she and a peer had engaged. In it, the competition gave both training and funding on how to push the idea out, get support, and advertise it to introduce transformational innovation into industry.

Marinaro offered a story regarding the integration of the Cirrus aircraft parachute system into aircraft which has proven to be a successful transformation in flight safety and resulted from an accident where it was clear the life-saving system was needed for pilots.

Inspiration for Innovation

Cavolowsky asked: “Our world is filled with so many issues and problems. How does one go about finding purpose or fulfillment in solving them?” Torres-Nieves’ suggested, “Do what you love.” She recommended aligning purpose with what you do – not that it’s not frustrating or challenging, but that you persist in spite of the challenges. Meanwhile, Marinaro agreed and expressed that one should continue to learn, to press forward. He said, “80% on time is better than 100% late.” To remedy this, he posed that individuals strategize realistic goals and pursue them to the finish, not necessarily to perfection.

View the entire panel above or on YouTube here.

“At the end day, we’re not going to replace the drivers. We’re just gonna augment them and make it safer.”

John Marinaro, Director of Fleet Operations

Understanding the Levels of Autonomy: 3-4-5