Commercial vehicles - important drivers for innovations

Autonomous driving Commercial vehicles - important drivers for innovations

2024-04-09 From Alexander Bodensohn* | Translated by AI 5 min Reading Time

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The logistics industry is constantly facing new challenges - driver shortages, falling profit margins, and not least climate change, which is also caused by the industry. However, there is also tremendous potential to address these issues and tackle them through innovations in automation.

Trucks are currently at the third level of driving automation.(Image: Aurora Labs) — Trucks are currently at the third level of driving automation.
(Image: Aurora Labs)

Alexander Bodensohn is Director of Business Development at Aurora Labs.

Compared to passenger vehicles, commercial vehicles face very specific challenges - whether due to their size, the kilometres to be covered, or the complexity of using autonomy in logistics. A lot of work is being done to future-proof these vehicles. However, these issues are not necessarily a disadvantage in the field of innovation.

In the world of commercial vehicles, there are remarkable advances that lead to impressive new capabilities through new possibilities in sensor technology and data processing.

Driver Assistance Systems

Modern trucks, like passenger cars, are now equipped with advanced driver assistance systems (ADAS). In total, there are six different levels:

Level 0 means that there are no supporting systems installed in the vehicle, i.e., the entire dynamic driving task (DDT) is performed by a human.
At level 1, the autonomous driving system performs an operational design domain (ODD) specific part of the DDT. This means either lateral or longitudinal control, i.e., either steering or accelerating/braking, but not both at the same time.
At level 2, partially automated driving is similar, but with the extension that the autonomous driving system can perform both lateral and longitudinal driving.
Level 3, conditional automation, is a further addition; the autonomous driving system can now perform a larger part of the Dynamic Driving Task (DDT) specific to the Operational Design Domain (ODD) in expectation that the driver is ready to drive when the system requests it. In addition, the autonomous driving system also performs the Object or Event Detection Response (OEDR), i.e., the system recognizes the environment and acts according to the requirements.
At Level 4, high automation, the autonomous driving system now performs the entire DDT within a certain ODD as well as the DDT fallback. This means that the human on the driver's seat is more of a passenger within the specific ODD. However, the human takes over the DDT when the autonomous vehicle leaves the ODD. An example of this is an autonomous vehicle capable of performing the DDT on a highway. But it cannot get on or off the highway itself, but requires a human driver who is capable of performing these tasks.
The final level is level 5, full automation, i.e. the non-ODD-specific execution of the entire DDT by an autonomous driving system the entire DDT and DDT fallback. This means that the autonomous vehicle can be used anywhere a typically trained human driver could drive the vehicle.

Trucks are currently at the third level of automation. Systems, such as the lane-keeping assist and collision reduction, are mainly for the safety of the drivers and other road users. However, due to the size and weight of trucks, additional safety features are needed. Examples of these are the brake hold mode, which relieves drivers during longer standstills, and the automatic stop function, which actively brakes the truck to a safe standstill.

Autonomous driving and platooning

The development of autonomous trucks is progressing rapidly. This is due to the fact that commercial vehicles mainly operate on motorways/highways, where the environment and manoeuvres are less complex. Compared to urban or rural roads, where passenger cars operate, motorway driving is more predictable. Especially in North America or Australia, intercity roads are often very controllable areas of action, in which automation comes into question. Although many tests still include drivers who can intervene if necessary, autonomous trucks are becoming increasingly likely through new hardware innovations that effectively use LiDAR, camera and radar systems, as well as powerful control units that could operate around the clock.

The advantages are obvious. Trucks that do not require a driver are more lucrative for freight companies. But even a relaxation of rest period regulations can lead to efficiency gains.

Despite promising results from truck platooning tests, where a convoy of trucks drive closely behind each other to reduce air resistance and be more efficient, this is more a form of automation than full autonomy. The driver remains necessary, especially when a vehicle separates from the convoy and has to navigate individually to the destination. Some emergency systems, such as the emergency brake assistant, are already mandatory for trucks today and have had to be installed in all new trucks since 2015.

However, the situation is different in enclosed environments. On enclosed premises, where no uninvolved persons are present and thus unpredictable risks are minimized, commercial vehicles can be used fully autonomously. By equipping the environment with sensors, sufficient data can be collected to ensure safe traffic. This area of application is particularly interesting for logistics centers and agriculture. There, large areas are available without external distracting factors, offering the ideal space for fully automated vehicles.

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Predictive Maintenance

With the help of sensors in the vehicle and AI tools, the maintenance needs of a truck can be precisely predicted. This means that technicians no longer need to be sent into the field, but the vehicles can be serviced when they are back at the base. All data, such as weather, route, temperature, and traffic are processed and thus determine the condition of the vehicle.

By predicting a vehicle's maintenance needs, fleet managers can ensure that these routes are covered by other trucks to minimize downtime. Additionally, this type of maintenance also improves safety. For example, tire sensors in combination with predictive maintenance algorithms could help prevent tire blowouts.

Software Innovations

While the hardware is important, it is the software that controls all of these intelligent functions. Almost every control unit/subsystem of a vehicle is significantly controlled by software today. This makes commercial vehicles incredibly complex, necessitating software innovations to ensure that the systems run without problems. After all, downtime is costly in terms of resources.

Intelligent software development tools are therefore necessary to ensure that the system integration is validated throughout the vehicle's life cycle; OTA updates should be carried out without downtime and without interruption of the vehicle's productivity; in addition, continuous monitoring of software behaviour should be carried out, even while driving. This detects system malfunctions before they lead to downtime and thus unnecessary costs.

The use of AI has now become an essential part of this development and maintenance process, helping to identify changes in software code lines, behaviour and relationships within a vehicle. AI provides important insights into the processes of software development, testing, integration, approval, deployment and maintenance. This accelerates development, reduces costs, and at the same time ensures safe, reliable, and robust software systems.