Vehicle Control Systems: Integrating Edge AI and ML for Enhanced Safety and Performance
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AI-driven dynamic trajectory planning and control systems are the keys to enhancing safety and performance for the next generation of autonomous vehicles. The growing demand for autonomous vehicles is pushing relevant companies to deploy the latest AI/ML methods to build better, more reliable, and versatile control systems. Current software architectures supporting the deployment and execution of AI in vehicles rely on centralized or decentralized control. Centralized approaches optimize performance over specific tasks, but they are poorly scalable. Decentralized approaches target scalability but struggle to maximize global efficiency and safety, especially when handling the variability and unpredictability associated with real-world scenarios. In this talk, we bring on the discussion that modular software architectures offer a more appealing way to organize the three core functions of future autonomous vehicles, i.e., sensing, planning, and control.Moreover, fostering the debate and collaboration between companies, academic institutions, and community-driven open-source foundations is a key priority to increase the number of potential solutions from a vast array of currently applicable technologies such as Deep Reinforcement Learning, Model Predictive Control, and Motion Planning Field, to name a few. The scale needed for a production-worthy solution is not achievable by any single company. Finally, an increasing level of democratization and standardization has a desirable side effect for the community itself: making the final user confident in the performance and safety of AI-driven products is the key to unlocking the adoption of fully autonomous vehicles.
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