Type
Autonomous Vehicle Simulation Platform
Objective
To reduce the physical testing costs of developing an autonomous vehicle and ensure safer deployment, one of the leaders in autonomous vehicle development wanted to
build a test simulation platform for autonomous technologies. The aim was to simulate traffic conditions created by real-world interactions between vehicles, road conditions, and weather environments.
Technologies
Python, AI, React.js, Azure, Docker, Jenkins, SQL, SAS, R
Country
Netherlands
Project Attributes
Engagement Model
Dedicated Team Engagement
Duration
20 months
App Users
Vehicle Engineers, Testers, Data Scientists, and Developers
Challenges
- Complex Real-World Simulations: The client required high-fidelity simulations that
could mimic the reality of real-world driving in its complexity: road terrace variability,
traffic behavior, and weather conditions. - High Computations: The task of building self-driving car simulators poses the challenge
of extreme computational capabilities, which are essential for handling huge amounts of
data, computing real-time vehicle interactions, and evaluating performance metrics. - Continuous Testing and Integration: The client needed a continuous integration
pipeline for testing so that new features landed with minimal downtime and updates were
done in a faster way. - Data Management: The complexity of the data from a simulation was so voluminous
that it could no longer be managed and analyzed for decisions or improvement. Efficient
storage and retrieval mechanisms were, therefore, required in this context.
Solutions
- Realistic simulation using AI and Python: Adorebits developed an AI-driven
simulation engine utilizing Python to simulate driving scenarios realistically. The platform
used AI models to predict behaviors in traffic, determine obstacles on roads, and
simulate real-time decision-making. - Cloud-based hosting: All the simulation platform was hosted on a cloud-based
infrastructure through Microsoft Azure, to enable high scalability computing power with
the handling of many complex simulations and heavy computational loads. - Containerization and continuous integration: Using Docker and Jenkins, the
company was able to provide the client with a seamless CI/CD pipeline. This allowed for
fast iteration of code and continuous testing while deploying new features faster with
minimal manual intervention. - Data Processing with SQL, SAS, and R: Data was processed using SQL, SAS, and R
where massive simulation data were stored and processed using SQL databases, while
advanced analytics and data visualization were done using SAS and R to give the client
deep insights into the performance of their autonomous vehicles. - Frontend with React.js: Adorebits created a user-friendly frontend with React.js,
exposing engineers and testers to an effortless configuration of simulations, view of
results, and easy changes to parameters while testing.
Results:
- Reduced Testing Costs: The customer could rely only on costly physical tests by simulating thousands of driving scenarios, but their testing costs were down by 50%.
- Faster development cycle: The continuous integration environment allowed the team to test new features and deploy updates much faster, saving them 30% of the development cycle time.
- Better Accuracy by Simulations: The nature of the AI-driven models, along with the strength of the data processing, guaranteed better accuracy in such simulations.
- Increased safety: By running large amounts of simulations, the client would have been able to foresee and reduce the danger of hazards; thus, their self-driving cars would have increased overall safety before an actual deployment in public.
Conclusion:
Adorebits successfully delivered a comprehensive autonomous vehicle simulation platform that meets the client’s objectives, changing the face of their testing and improving the safety and reliability of autonomous vehicle technologies. This solution places Adorebits as the leader in advanced simulation technologies for the autonomous vehicle industry.