Self-Driving Speed Planning via Reinforcement Learning State Table

The patent pre-calculates and stores optimal speed planning data in a state table during the offline training phase using reinforcement learning. During online execution, the system only needs to perform table lookup and interpolation operations, which are computationally lightweight and can be completed within real-time constraints. This preliminary action transfers the heavy computational burden from online to offline, resolving the contradiction between achieving global optimality and meeting real-time calculation requirements.

The patent creates a simplified copy of the complex optimization problem by training an agent to learn the optimal speed planning policy and storing it in a state table. Instead of solving the original complex optimization problem in real-time, the system copies the solution into a lookup table format that can be quickly accessed during execution. This copying approach preserves the global optimality while dramatically reducing online computational requirements.

The system pre-calculates safe and optimal speed planning strategies for all possible states in the state table during offline training. This preliminary action ensures that safety constraints are built into the planning logic beforehand, eliminating the need for complex real-time safety checks while maintaining high reliability. The offline training phase allows the system to learn from extensive simulation data, capturing safety-critical scenarios that single-time control would miss.

The patent uses reinforcement learning with reward functions that incorporate safety constraints and optimization objectives. The feedback mechanism guides the agent to learn policies that balance safety and efficiency. During online execution, the learned policy automatically adapts to different scenarios, providing reliable speed planning decisions without requiring complex real-time safety validation, thus maintaining both productivity and reliability.

Instead of using a complex neural network model, the patent copies the learned policy into a simple state table format. This table can be quickly accessed during real-time inference without requiring heavy computational resources. The copying approach transforms the continuous neural network output into a discrete lookup table that is much more efficient for embedded systems, reducing both memory usage and computational overhead while maintaining real-time performance.

The patent replaces expensive neural network computations with cheap table lookup operations. The state table acts as a lightweight, pre-computed solution that can be quickly queried without requiring powerful hardware or extensive computational resources. This substitution makes the system suitable for resource-constrained embedded platforms while maintaining real-time inference capabilities.