Autonomous, or self-driving cars will make driving safer, smarter and more efficient. Equipped with internet connections and often wireless local area networks, self-driving cars can speak to each other and the environment around them.
Autonomous cars will include features such as lane assist, collision avoidance and automatic calls to emergency services to create a safer driving experience. One feature allows the cars to keep their distance and synchronise braking to avoid accidents. Some automotive brands are taking automation a step further, innovating to enable vehicles to communicate with nearby infrastructure and even pedestrians, adding a new dimension to how automation can benefit society.
To develop and deploy autonomous cars at scale, the right technology infrastructure is critical. Production of these vehicles requires high-performance computing capacity, along with seamless cybersecurity and the ability to manage vast data sets. This is why many organisations are taking advantage of the numerous benefits offered by the cloud, including cloud infrastructure’s reliability, scalability and security.
Training and testing
Autonomous cars strive to create vehicles that operate more safely than humans. Delivering on this ambition requires extensive modelling and testing. The ability to collect, store, and manage data is critical, as are advanced machine learning techniques.
Toyota Research Institute (TRI) believes that accurately training autonomous cars requires trillions of miles of testing. To deliver on this, it has a fleet of test cars equipped with Light Direction and Ranging (Lidar) Sensors to record data, collecting terabytes of data every day, needing quick retrieval and analyses.
TRI uses AWS to manage this data and access the processing power required to train machine learning models quickly. Using cloud infrastructure, it has gained the ability to spin up compute and storage resources on demand and blend these with management and orchestration services. TRI now retrains vehicle models, increases accuracy, and introduces new features faster. By following similar models, more automotive businesses will accelerate the development of safer cars.
Edging forward
Enabling autonomous cars to make rapid, data-driven decisions will make our roads safer. These machines need backing by reliable infrastructure with low latency and high availability. They also need to analyse information in real time, including data on road conditions, weather, and the behaviour of other vehicles. Applying AI allows the car to react swiftly and safely to road conditions.
Edge computing allows this core in-car technology to perform its in-car data crunching. When a second of lag can make the difference between a safe or dangerous response, autonomous vehicles do not have the luxury of waiting for data processing in the cloud. Therefore, organisations need to look for cloud providers with integrated edge solutions. These allow analysis of mission-critical data at the source and reducing the cost of transmitting additional data to the cloud.
Security in the cloud
Cybersecurity is important when developing safety-focused autonomous cars. Each vehicle becomes a new endpoint which must be secured. Protection from hackers and malware must be a top priority to ensure they cannot gain access to driving controls or the data that runs through each vehicle.
Autonomous car producers are turning to the cloud in order to support and run connected cars. It’s the job of the cloud provider to ensure cybersecurity updates and upgrades regularly happen. Additionally, the best cloud providers deliver automated security services which apply machine learning to proactively manage tasks including security assessments, threat detection, and policy management. By having security baked in, manufacturers can be confident that they have the solutions in place to detect new and emerging vulnerabilities and threats which will reduce harm to drivers and lower the risk of a breach.
Connected cars, cloud and security
Autonomous cars are our future; however to drive adoption, manufacturers must ensure they are secure and supported by robust infrastructure. Using the right cloud vendors is critical, enabling organisations to focus their resources on building differentiated automotive experiences, rather than managing IT infrastructure.
With this in mind, AWS provides a full suite of services to support autonomous vehicle development and deployment. AWS provides a full suite of services to support Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicle development and deployment. AWS' nearly unlimited storage and compute capacity and support for deep learning frameworks such as Apache MXNet, TensorFlow and PyTorch accelerate algorithm training and testing. AWS Greengrass provides edge computing with machine learning inference capabilities for real-time processing of local rules and events in the vehicle while minimising the cost of transmitting data to the cloud. A combination of scalable storage and compute capacity and support for deep learning frameworks help accelerate testing and service development. At the same time, its agile platform helps businesses accelerate the pace of innovation, improve security posture and lower IT cost structure.
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