What Is Connected Car Data?
In this article John Cartledge, Global Development Manager and Joffroy Pirlot, Product Owner at Aisin explore the divergent meanings and applications of Connected Car Data and place it in a meaningful context for those ensuring safety on our roads.
Many car users today are aware that their car has some internet connectivity through regular use of in-car features such as on-board satellite navigation systems and broad uses for personal smart devices compatible with the car (as just two examples). What is perhaps not so well known is that the vast majority of cars produced today generate a much broader range of data from various features on the vehicle, much of which they will never see or be aware of.
The ability to share internet access and all this different data with other devices inside and outside the car is what gives them the classification of ‘connected cars’, with 91% of all new vehicles sold in the US in 2020 falling under this classification.
In truth, the introduction of on-board car diagnostics in the 1990s can be seen as the beginning of this trend, primarily to enable emergency calls following an accident.
However technological advancements and the increasing complexity means that connected car data attributes generated from vehicles today have many variables – location, speed, engine status, security features and more. This data is generated from a multitude of sources including the vehicle’s engine control units (ECUs), Controller Access Networks (CANs), and even in-built entertainment systems.
We can classify this data in different ways, we will focus on the differences between floating car (GPS) data and Vehicle Sensor data, including from the CAN, and outline the distinct meanings and uses for them.
Floating Car Data
Floating Car Data comes from vehicle GPS signals, one of the data sources embedded within the vehicle. It is commonly understood that GPS tracking works using a network of satellites to determine the physical location of an object (in this case, the vehicle) by trilateration. This technology finds common use in our everyday lives, for mapping, monitoring, adventuring and more.
Its main use in this context is for finding and monitoring the direction and location of a vehicle to identify traffic average speed over portions of roads.
This type of data is typically used to identify traffic patterns and flow, including congestion, by plotting geolocalised average speeds across road networks. However, the limitations of Floating Car Data lie in its inaccuracy when it comes to analysing driver behaviour or precise events, such as harsh braking and other abnormal or dangerous behaviour. It can give us a macro view of where vehicles are and where they are clustered (and which direction they face) but it can’t give us any deeper insights about the vehicle or driver behaviour.
Vehicle Sensor Data
Understanding actual driver behaviour is made possible by harvesting Controller Area Network (CAN bus) data from vehicle sensors, such as the vehicle’s accelerometer.
This CAN data is a robust vehicle bus (a specialized internal communications network in vehicles) standard designed to allow microcontrollers and devices to communicate with each other's applications without a host computer. It is this data that can be used (anonymously) to give greater insight into driver behaviour by showing us things like harsh braking and accelerating from the onboard accelerometer.
Automotive industry specialists such as Aisin harvest millions of Vehicle Sensor data traces from different types of vehicles and analyse trends in the data to produce fascinating insights into collective driving behaviour. The high-risk areas where driver behaviour is repeatedly noted as erratic (for example repeated incidents of harsh braking in a given stretch) can be precisely geolocated on a map of the road network and twinned with other data sources available and flagged for investigation by trained road safety professionals.
As previously noted, this type of analysis of driver behaviour is not possible with floating car data, it gives a much deeper insight into the driver experience and specific events on the road. When twinned with other contextual data (such as Gaist’s high-definition imagery of the area, viewable through their data platform) this can provide a far deeper level of insight with regards to road safety, giving those responsible a valuable new tool in their arsenal in maintenance and prevention activities.
In knowing where the repeated harsh braking is on your road network you can react much quicker and put your expertise where it is needed, potentially saving lives in the process. The Aisin-Gaist partnership presents one of the first applications of Vehicle Sensor data tailored specifically for the roads safety professional as the end user, taking masses of data and realising it’s potential in a meaningful and actionable format.
We’d love to continue the discussion on road safety and connected car data- get in touch at email@example.com