Vehicles with self-driving technology can bring many benefits to society. One of the top priorities at Toyota Research Institute (TRI) is to apply the latest advancements in artificial intelligence (AI) to help Toyota produce cars that are safer, more accessible, and more environmentally friendly. To help TRI achieve their goals, they turned to deep learning on Amazon Web Services (AWS).
Using Amazon EC2 P3 instances, TRI is seeing a 4X faster time-to-train than the P2 instances they had used previously, reducing their training time from days to hours. This gives them significant agility to optimize and retrain their models quickly and to deploy them in their test cars or simulation environments for further testing. In addition, the significant performance improvement in P3 instances over P2 instances, coupled with the AWS pay-as-you-go model, translates to lower operating costs for TRI.
TRI is developing a single technology stack for their automated driving technology with two modes: Guardian and Chauffeur. Guardian mode requires the driver to have hands on the wheel and eyes on the road at all times, while it constantly monitors the driving environment, inside and out, intervening only when necessary when it perceives a potential crash. Chauffeur mode uses the same technology, but is always in control, and vehicle occupants are strictly passengers.
Developing and deploying autonomous vehicles requires the ability to collect, store, and manage massive amounts of data, high performance computing capacity, and advanced deep learning techniques, along with the capability to do real-time processing in the vehicle.
Using the PyTorch deep learning framework, TRI created deep learning computer vision models to automatically provide monitoring and control in both driving modes. To gather data used in their deep learning models, TRI has a fleet of test cars equipped with various types of data acquisition sensors such as cameras, radar, and LIDAR (a technique used in control and navigation to generate object representations in 3D space). These test vehicles drive through various Operational Design Domains (ODD), collecting and recording data, which amounts to terabytes of data per day per car. This data needs to be quickly retrieved, prepped, and made available for analysis and retraining of machine learning models and simulations.
TRI believes that accurately training models requires of miles of testing. With over 100 million Toyotas on the road today, drivers experience a range of driving conditions. To complement their vehicle testing, TRI uses simulations to model a variety of rare conditions and scenarios. These simulations generate photo-real data streams that test how their machine learning models react to demanding cases such as rainstorms, snowstorms, and sharp glare at different times of the day and night, with different road surfaces and surroundings.
As new test data becomes available, TRI rapidly explores research ideas and trains their models quickly so they can deploy updated versions on their test cars and rerun tests.
“Using Amazon EC2 P3 instances, we reduced the time to train our models by 75%.
