Advanced Sensor Technologies for Autonomous Trucks: Pioneering the Road to Safe and Efficient Long-Haul Trucking
Published: 2023-10-03 13:06:21

The realm of transportation has witnessed a transformative evolution that is targeted to redefine the very fabric of our roadways. Autonomous trucking, a concept that once seemed confined to the pages of science fiction, now becomes more and more real. The pace at which this technological revolution is unfolding is nothing short of astonishing, with each passing day bringing us closer to a future where long-haul trucking operates seamlessly without the need for human intervention.

We aim to find out what sets the stage for this remarkable shift towards autonomous trucking. Why are trucks, in particular, emerging as the perfectstart for the autonomous future of transportation? The answers to these questions lie in the convergence of numerous factors, where the synthesis of technology, safety, and efficiency has taken center stage. At the heart of this transformation lies the primary role of sensor technologies, which have become the basis upon which the edifice of autonomous trucking is being constructed.

This article embarks on a journey to illuminate the cutting-edge sensor technologies that are propelling long-haul autonomous trucking into uncharted territory. Our aim is to untangle the mystery of sensors, understand their significance, and shed light on the transformative impact they have on the world of transportation. We will explore the mechanisms, innovations, and challenges that come together to pave the way for safe, efficient, and truly autonomous long-haul trucking.


## Underlying Technologies: The Sensor Revolution

The evolution of sensor technologies has been preeminent in the development of autonomous trucks. These technologies promise safer and more efficient long-haul trucking. Let us start by looking at what comprises what we call sensor technologies. 


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ez4tmll6qjemxi4j9se5.PNG)

LIDAR, which stands for Light Detection and Ranging, uses laser light to measure distances. By transmitting a series of laser pulses and measuring the time they take to return after reflecting off objects, LIDAR can generate precise, three-dimensional maps of the surrounding environment. With the decline in costs and increased miniaturization, LIDAR sensors have become vital in autonomous trucking: they offer superior resolution in diverse weather and climate conditions. _Data from the LiDAR is very accurate depth information and has higher resolution than classic radars. LiDAR's modulation and other technical details we will leave to another separate article about it._ 


Beyond real-time navigation, LIDAR plays a crucial role in high-definition mapping, which helps trucks anticipate the road ahead. Its pinpoint accuracy ensures trucks can detect obstacles, from other vehicles to pedestrians, which in turn provides safer journeys.

_However, the LiDAR has a few minuses. They are very expensive. The price of a high-end set of LiDARs for an autonomous vehicle could cost a new Hyundai. Finally, air particles such as dust and rain or fog create noise on the LiDAR data._ 


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/w4stqec2p5n5iy0pt3qu.PNG)

Radar, an acronym for Radio Detection and Ranging, isn't a novice. It was originally developed for military and weather prediction applications, but nowadays radar has found a place in vehicle safety, especially with adaptive cruise control.

Unlike LIDAR, radar uses radio waves, making it able to detect the speed of distant objects. It has the capability to function well in dense fog, rain, or snow, which also makes it indispensable for autonomous trucks. Modern radars can detect vehicles ahead and adjust the truck's speed accordingly. In emergency scenarios, radar systems can trigger automatic braking, substantially reducing collision risks.


_However, sensors always have pros and cons. Radar has low resolution. See the picture of LiDAR and Radar below._

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/t7g87n9eau0tk2i7icqc.PNG)

Cameras offer a rich source of visual data: they can interpret traffic signs, lane markings, and even the intentions of nearby drivers or pedestrians. Advancements in image processing algorithms allow these cameras to distinguish objects, even in low-light conditions. Paired with machine learning, they can make informed decisions based on patterns and experience. _Modern computer vision algorithms_ augment the raw data from cameras, enabling the truck to understand complex scenarios, like intersections or a pedestrian propelling onto the road.


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/n1rczytctg90p757u0gu.PNG)

As we discussed earlier, cameras have some limitations as well. A vehicle needs around 8 cameras to cover the blind spots, and 8 cameras generate around 1.8GB raw data per second (regular 1080p with 60Hz). It means the autonomous vehicle should have the computational power to process this data in less than a second and make decisions based on this data. 

**Others**

Of course, the autonomous vehicle has a lot of other types of sensors. Such as GPS for example. GPS provides global positioning data, while odometry, which estimates position based on wheel rotations, fills in the gaps when GPS signals are weak or obstructed. Together, they ensure the truck stays on course. 

Ultrasonic sensors detect nearby objects, ideal for maneuvering in narrow spaces, while infrared sensors enhance visibility in the dark. In synthesis, all these sensors show that autonomous trucks are not just futuristic concepts.


## Challenges and Solutions


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**Navigating Through Adverse Conditions**

Sensors, while advanced, aren't flawless — tough weather conditions can significantly reduce their effectiveness. Recent innovations focus on developing sensors that can adapt to such conditions, altering their frequencies or employing techniques that allow better obstacle passing. One sensor might trip, but when multiple sensors work in synergy, the chances of oversight are reduced. Integration of data from LIDAR, radar, cameras, and other sensors ensures that trucks can get a more comprehensive view of their surroundings.

**Data Management and Processing**

Autonomous trucks are data giants. With sensors capturing information every millisecond, managing this influx is the main action. Advanced storage solutions, both onboard and cloud-based, are crucial for retaining this data.

_We already discussed the cameras with 1.8GB per second. The LiDARs from the other hand produce 1.3 million readings per second with rotation at 10Hz. In most cases, the autonomous vehicle has 3 or even more LiDARs. Process of overlay one lay of data to another or sensor fusion are required high computing power. And don’t forget that you should do it in real time._  

Other than that, with vast data comes the challenge of processing. Thanks to state-of-the-art AI algorithms, trucks can now sift through this data in real time, making fast decisions that ensure safety and efficiency. The instance is [Porsche's recent collaboration](https://newsroom.porsche.com/en/2021/company/porsche-vodafone-cooperation-5g-standalone-network-weissach-development-centre-real-time-mobile-communications-25566.html) with Vodafone and HERE Technologies to integrate standalone 5G at its Weissach Development Center, which underlines the critical role of 5G in enhancing Advanced Driver Assistance Systems (ADAS) through real-time data analysis and warning systems.


**Regulatory and Safety Considerations**

As with any groundbreaking technology, the regulatory landscape is continuously evolving in the case of trucks. Safety is for sure at the top of the list. The primary goal of these sensors isn't just autonomy — it's safety. Manufacturers and operators should constantly refine sensor technology, ensuring that autonomous trucks not only match the safety standards of human-driven vehicles but surpass them. 

Nevertheless, it’s hard to hold forth about things like data management, security, and regulations without looking at specific examples of developments in the field of autonomous vehicles. Let's dive deeper into this and see: who are those industry giants who make the future of autonomous trucking our present reality?


## Case Studies
Leaders in the autonomous trucking realm, such as Waabi, Torc Robotics, Volvo, Gatik and dozens of others, have been instrumental in implementing advanced sensor technology. 

As an example, [Waabi has recently unveiled trucks purpose-built for OEM integration](https://waabi.ai/introducing-the-waabi-driver/), making a significant step forward in commercializing autonomous solutions. These vehicles are enhanced by the Waabi Driver, a fusion of their software, sensors, and computing. Meanwhile, Waymo, after six years of initial autonomous system testing in Class 8 trucks, is taking a renewed focus on [ride-hailing using driverless vehicles](https://waymo.com/waymo-driver/). [Sberautotech](https://motor.ru/news/self-driving-27-06-2023.htm) has launched unmanned cargo transportation along the M-11 Neva highway. Tractors will run from Moscow to St. Petersburg and back.

Startups are not merely following in the footsteps of industry giants but are charting their own paths. Specializing in LiDAR sensors, [Tyriad's](http://tyriad.com/) AI-based cloud infrastructure, Pulse, empowers vehicles to tap into a shared network of information, enhancing decision-making on the road. It also promises dynamic surroundings detection and accelerates the development and deployment of autonomous vehicles by OEMs. These and numerous other companies have a huge potential to disrupt the market flexibly to changes. 


## Future Potential of the Sensors: Beyond Trucking


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/54g701inx9p6uy4jbh61.PNG)


Besides promising development in such areas as quantum mechanics, the deployment of sensors is already stretching far beyond trucking. Sensor technologies are finding their way into varied sectors: a paradigm shift with sensors in agriculture aiding in precise farming, ensuring optimal usage of resources and maximizing results. In the construction sector, these sensors enhance safety, efficiency, and structural integrity. Furthermore, environmental monitoring benefits immensely, with sensors facilitating real-time data collection, providing a sustainable and balanced coexistence with nature.

_The self-driving cars are not something absolutely new. However, the first worldwide implementation of autonomous transport will be as autonomous heavy trucks. It’s not a secret that rural environment has less diversification of road scenarios than in a city. 90% of transportations are between the big cities with well-developed infrastructure. Based on my expertise in a market, the limited operational domain already successfully conducted. Some small tunes of technology could help the whole industry to leap ahead.  Not the last word of sense here will be economy. The commercial trucks are designed to work 24/7. If we eliminate the human restrictions here as limited time of attention and working hours, we could increase the profit of transport companies._ 

In wrapping up our exploration of the sensor revolution, it's no secret that the advancements in sensor technologies have been monumental for the trucking industry. From the insights seen throughout this article, we recognize the transformative potential these technologies bring to the table: safer vehicles without human intervention, effective data management and navigation, and further diverse applications in the areas beyond trucking. We anticipate a paradigm where long-haul trucking is not only safer but also more efficient, minimizing risks and maximizing productivity.