You're reading Part 5: Challenges for Connected Vehicles

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Education gaps

One of the largest challenges for connected vehicles and vehicle-generated data is education. The education gap exists on the side of car owners, the general public, and private organisations that want to leverage this data for value creation.  This includes education about what a connected vehicle is, the data, how the data is collected, how this relates to individual privacy, use cases, and best practices.

Data privacy, sovereignty, and differences in legislation

A second challenge for connected vehicles is perceptions around data privacy and security, data sovereignty, and legislative differences across different markets. For instance, GDPR legislation is regarded as one of the most comprehensive security laws in the world, covering accuracy, storage limitations, integrity and confidentiality, and accountability. Data providers must ensure they abide by any legislations governing the use of data.

Data sovereignty - maintaining authority over data within set jurisdictions - is another factor to consider. There must be an absolute guarantee from manufacturers who hold connected vehicle data that protection and control of any datasets is maintained.

Industry acceptance and integration of data

Another challenge is industry acceptance, trust, and integration of vehicle-generated insights. Part of addressing this challenge falls under a lack of educational resources and single-source of truth. As connected vehicle technology becomes more well-known, better understood, and more trials of the data in the real world are completed, this will positively impact industry acceptance of connected vehicle data as a legitimate source of information.

Data latency

Delays in data transmission can impact the timeliness of the information and can be life-threatening in situations that involve emergency vehicles or safety concerns. Latency is being addressed by the introduction of either ITS technology or technology that helps ITS infrastructure such as 5G, AI, Machine Learning, better sensor interconnectivity, edge computing, and transmitters to bolster signal strength in areas with coverage issues.

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