Self-driving vehicles – why telematics will be critical to their success

Many will be aware of the success Tesla has had in developing self-driving vehicles. While they are not fully there yet, its technology is constantly improving to the point where its cars fitted with this technology can largely drive themselves with the human driver there to intervene when necessary.

But other manufacturers are catching up, and anybody who has bought a car in the last few years may find some autonomy in it. This could be automatic breaking in emergency situations, lane assist devices or smart cruise control.

Commercial vehicle manufacturers are also investing in self-driving technology with the aim of developing fully autonomous vehicles. For example, Volvo is testing trucks that have self-driving capability.

While Waymo, the sister company of Google and part of Alphabet, has been running driverless taxis in Phoenix, Arizona since October 2020. It is also working on self-driving capability for trucks too.

Start-up TuSimple drove an autonomous truck on public roads for 80 miles from a railyard in Tuscon to a distribution center in Phoenix in December.

These technologies are on their way and before long, it is inevitable that recycling and waste management companies will start to use them.

I imagine that we will see more self-driving technology in waste and recycling trucks over the next few years, and fully autonomous technology within a decade.

Telematics will be crucial for each stage of this. For example, I would expect artificial intelligence (AI) and machine learning (ML) to be integrated with self-driving and telematics technology.

Vehicles will know much more information, such as identifying weather conditions that could impact vehicle safety or the availability of routes. Vehicles will use AI and ML to identify potential maintenance issues before they are noticed by existing sensors. Indeed, maintenance teams will be able to go back and check when an issue occurred on a vehicle and identify the operating conditions that caused it. Having information like this will lead to helping drivers identify road conditions or driving styles that led to maintenance issues, as well as allowing for better prediction of when parts will need to be replaced.

When we get to the point of fully-autonomous recycling and waste trucks, we will need to know where the vehicle is and this is where telematics will play a crucial role. On top of this, telematics will work with self-driving technology to identify potential hazards and ensure the vehicle drives in a safe manner.

If a vehicle breaks down (and hopefully in most cases this will have been prevented by AI and ML working in conjunction with telematics to predict the issue), then telematics will help identify where the vehicle is. Then it can be repaired on the roadside or recovered.

With the rapid roll-out of 5G mobile communications technology in many countries, we will be able to share more telematics data more rapidly with controllers back at base. This will allow a greater understanding of where vehicles are, the hazards they face, and allow major adjustments to be made to routes if necessary.

Integrating telematics with optimization

Self-driving electric vehicles will still need to be controlled and optimization platforms and telematics will work together for that purpose.

Data received from telematics devices will enable effective planning and optimization of routes, as they do now.

But telematics will help inform the capability of electric vehicles to complete a route, taking in the state of charge, any necessary mid-route charge time, and any conditions that may affect range such as cold, wet weather.

Data from self-driving vehicles will help to inform route optimization, using AI and ML to spot trends such as traffic conditions and vehicle loads. Vehicles should also be able to monitor resistance due to climbing hills and regenerative braking downhill to better calculate range on a route.

5G networks will allow real-time monitoring of the vehicle to ensure it is on plan and enable rapid adjustments to be made. This might be done automatically by the software allowing for minimal interference from the controller requiring intervention only when a human needs to make a decision.