These kinds of capabilities however turn self-driving cars into agents, conducting transactions.
This requires several technologies working in tandem: Internet of Things-connected sensors on vehicles and pervasive connectivity as well as digital exchange models like blockchain, which promise secure, paperless, transparent transactions. In this way, data is helping install enough information and deep learning in autonomous cars to make them safer for all drivers.
Due to the extensive amount of data generated by a connected and autonomous vehicle (CAV) and the information flowing among AVs and through surrounding infrastructure, data governance legislation poses a significant challenge especially in the perspective of their large-scale introduction.
Autonomous driverless car systems require optimization and complete vehicle performance validation.
To strike a balance of safety, comfort, and fuel, self-driving functions need insight into different scenarios, environments, and conditions. For modern or new age cars there are different computing sensors that collect massive amounts of data that get used to processing the various functions required to facilitate driverless mobility.
For self-driving cars to work, an ocean of data needs to flow into a lattice of sophisticated sensors. The car needs to know where it is, where it’s going, and be able to keep track of every other thing and creature on the road. This level of data collection is a natural extension of a driverless car’s functionality.
Technologies used on driverless cars are mostly LiDAR, radar, camera, and ultrasonic sensors. These sensors are used to collect data to sense the environment safely and provide the necessary redundancy. The car’s computers combine all of this to build a picture of where other cars, cyclists, pedestrians, and obstacles are and where they’re moving. For this part, lots of training data is needed — that is, the car needs to draw on millions of miles of driving data.
Data collection thus becomes a very important first step to determine the efficacy of self-driving cars and the data analysis in a sensor fusion engine is also essentially important to identify, compute and empower autonomous cars of the future with the right capabilities to perform their functions seamlessly.
It isn’t only the cars that will be able to use the data they collect. The true potential of this data is great:
An automotive manufacturer can remotely see a problem with your car and immediately inform you through your vehicle
With vehicle data, managing a fleet of hundreds or thousands of connected cars becomes an efficient and optimized process
Big data is a rich source of behavioural insights, from the petrol stations, drivers choose to the music they listen to. This information can be used in marketing, sales, and customer service
City planning and engineering will become more accurate with big data from connected vehicles: more efficient road planning, early warning systems on dangerous spots, and safer pedestrian walkways are just a few possible outcomes
Data from connected cars can help to develop custom insurance plans for drivers based on their performance.
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As autonomous vehicles are gathering large amounts of personal data about drivers, passengers, and other road users, a broad range of privacy and data protection questions arise. The impact of malicious security attacks that go beyond data or vehicle theft has the capability to cause significant damage and potential loss of life, along with a potential impact on manufacturer reputation.
Therefore, building trust in such technology amidst the growing concerns of security attacks and vulnerability exploitations is a challenge that needs to be addressed across the automotive industry. BS EN ISO/IEC 27001 helps you implement a robust approach to managing information security in your automotive business and to build resilience.
Other key cybersecurity standards related to the automotive industry include:
PAS 11281:2018 Connected automotive eco-systems – Impact of security on safety – Code of Practice. This PAS aims to help organizations in the Connected and Automated Vehicle ecosystem ensure that security-related risks in their products, services, or activities don’t pose an unacceptable safety risk in the physical world.
PAS 1885:2018 Specification of the principles of automotive cybersecurity. This PAS has been written to help all parties involved in the vehicle lifecycle and ecosystem understand better how to improve and maintain vehicle security and the security of associated intelligent transport systems (ITS).
BS 10754 Information security – Systems trustworthiness – governance and management specification.
BS ISO 20080:2019 Road vehicles. Information for remote diagnostic support. General requirements, definitions, and use cases. This standard specifies general requirements and constraints applicable to a remote diagnostic process, the use cases, and scenarios to support the implementation of a remote diagnostic process using a standardized interface of the ExVe.
BS EN ISO 18541 series (Parts 1-5) Road vehicles. These standards give automotive businesses access to automotive repair and maintenance information (RMI).
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