Reducing the computational complexity of bidirectional co-leader planning is essential for enabling real-time implementation in production autonomous vehicles. Here are several strategies to achieve this:
Hierarchical Planning: Implementing a hierarchical planning approach can help break down the planning task into simpler sub-tasks. By first generating high-level plans and then refining them into detailed trajectories, the overall computational burden can be reduced.
Sampling-Based Methods: Utilizing sampling-based methods, such as Rapidly-exploring Random Trees (RRT) or Monte Carlo Tree Search (MCTS), can efficiently explore the space of possible trajectories without exhaustively evaluating every option. These methods can focus on promising regions of the search space, significantly reducing computation time.
Pruning Techniques: Applying pruning techniques to eliminate less promising trajectories early in the planning process can help streamline computations. By setting thresholds for collision risk or comfort metrics, the planner can discard trajectories that are unlikely to be optimal.
Parallel Processing: Leveraging parallel processing capabilities can enhance computational efficiency. By distributing the planning tasks across multiple processors or using GPU acceleration, the system can handle complex calculations more rapidly.
Model Compression: Implementing model compression techniques, such as knowledge distillation or pruning, can reduce the size and complexity of the neural networks used in planning. Smaller models can operate faster while maintaining acceptable performance levels.
Adaptive Time Discretization: Instead of using a fixed time discretization for planning, adaptive time discretization can be employed. This approach allows the planner to allocate more computational resources to critical time steps while reducing the resolution during less critical phases, optimizing overall performance.
Use of Predictive Models: Integrating predictive models that can quickly estimate the likely future states of SVs based on historical data can reduce the need for exhaustive simulations. These models can provide a probabilistic understanding of SV behavior, allowing the EV to plan more efficiently.
By implementing these strategies, the computational complexity of bidirectional co-leader planning can be significantly reduced, enabling real-time decision-making capabilities essential for safe and efficient autonomous driving.