Parallel Beam Search for Simulated Agent Trajectories
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Solution Overview
Problem
Existing techniques for generating simulated trajectories for autonomous vehicles are insufficient in realism and diversity, often resulting in collisions or off-road scenarios, and may lead to mode collapse during training.
Innovation Solution
The implementation of a hierarchical approach combining parallel beam search with goal generation and goal conditioning to refine and diversify simulated trajectories, ensuring they are both realistic and representative of real-world scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing techniques for generating simulated trajectories are used, then the generation process is simple, but the realism and diversity of trajectories are insufficient, resulting in collisions or off-road scenarios
Solution Approach 1:
The trajectory generation process is segmented into multiple independent components: goal generation module, goal conditioning module, and parallel beam search module. Each component performs a specific function (generating goals, conditioning policies on goals, searching for optimal trajectories), allowing the complex task of generating realistic trajectories to be divided into manageable, specialized sub-tasks that can be optimized independently
Solution Approach 2:
Goals are generated in advance before trajectory generation begins. The goal generation module creates a set of potential goals based on the initial state, which then guide the subsequent trajectory generation process. This preliminary action ensures that trajectories are generated with realistic destinations and purposes, improving realism without requiring complex real-time reasoning during trajectory generation
2Reliability
If parallel beam search is used to improve trajectory realism, then trajectory quality improves, but trajectory diversity decreases due to mode collapse
Solution Approach 1:
Goals serve as intermediaries between the initial state and final trajectories. The goal conditioning module uses these intermediate goals to guide policy generation, ensuring that diverse goals lead to diverse trajectories. The parallel beam search operates conditioned on these goals, maintaining diversity by exploring multiple goal-conditioned pathways rather than collapsing to a single mode
Solution Approach 2:
The approach adds the dimension of goal space to the trajectory generation process. Instead of directly generating trajectories from initial states, the system first samples from a diverse goal space, then conditions trajectory generation on these goals. This additional dimensional layer (goal dimension) preserves diversity while enabling quality improvement through goal-directed optimization
3Quantity of substance
If a large number of simulated trajectories are generated, then training data availability improves, but computational cost and time increase
Solution Approach 1:
The parallel beam search performs continuous refinement of trajectories by iteratively evaluating and pruning partial trajectories. Rather than generating complete trajectories in one step, the system continuously improves trajectory quality by evaluating intermediate states and eliminating poor candidates, making the computational process more efficient and scalable to larger numbers of trajectories
Solution Approach 2:
The beam search process evaluates and prunes partial trajectories at intermediate steps, discarding only the portion of the search space that leads to suboptimal outcomes. This partial action (evaluating only necessary portions of the trajectory space) allows the system to generate many high-quality trajectories without exhaustively searching the entire state space, reducing computational time
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating simulated trajectories using parallel beam search.