Inference-Aware Motion Planning for Autonomous Vehicles
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Solution Overview
Problem
Existing motion planning systems for autonomous vehicles do not account for inferences about the environment outside of directly perceived information, leading to suboptimal trajectory planning.
Innovation Solution
An inference-aware motion planning system that uses dynamic obstacles data and inferred costs based on probabilities to adjust the total cost of different trajectories, incorporating data from various sensors and communication systems to generate a smooth trajectory for the vehicle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion planning systems use only directly perceived information from sensors, then the system complexity is reduced, but the trajectory planning becomes suboptimal and less safe
Solution Approach 1:
The inference module performs preliminary analysis of sensor data to deduce environmental conditions and potential obstacles before the motion planning executes. By pre-processing the data to infer meanings (e.g., inferring a closed drain from visual patterns, predicting pedestrian intentions from behavior patterns), the system prepares optimized trajectory options in advance, improving safety without proportionally increasing real-time computational complexity.
Solution Approach 2:
The inference module acts as an intermediary layer between the raw sensor data and the motion planning system. It transforms direct sensor observations into inferred environmental understandings (e.g., inferring road conditions, obstacle intentions, or hidden hazards), which then feed into the motion planning algorithm. This intermediary processing enhances trajectory safety while managing system complexity through modular architecture.
2Productivity
If motion planning systems account for inferred environmental information, then the trajectory optimization improves, but the computational processing time increases
Solution Approach 1:
The system performs inference computations in advance or in parallel with sensor data acquisition, preparing probable environmental interpretations before the motion planning decision deadline. By pre-computing likely scenarios and their associated costs, the system reduces real-time computational burden while maintaining high trajectory optimization quality.
Solution Approach 2:
The inference module focuses computational resources on the most critical and probable environmental factors rather than analyzing all possible scenarios equally. It performs partial inference on high-priority elements (e.g., inferring only pedestrian intent when a pedestrian is detected, rather than inferring all environmental conditions), thereby improving trajectory quality where it matters most while limiting computational time expenditure.
3Measurement precision
If the system calculates inferred costs based on probabilities of motion sets, then the decision-making accuracy improves, but the computational complexity increases
Solution Approach 1:
The system dynamically adjusts the precision and depth of probabilistic inference based on operational context. In high-risk situations (e.g., pedestrian crossings, intersections), it calculates more detailed inferred costs with higher measurement precision. In low-risk scenarios, it uses simplified probability models. This parameter adaptation maintains decision-making accuracy when needed while reducing computational complexity during routine operations.
Solution Approach 2:
The system applies full probabilistic inference only to the most critical motion options and environmental factors, rather than calculating inferred costs for all possible motion sets equally. It performs partial probability analysis on high-stakes decisions while using deterministic or simplified models for routine maneuvers, thereby improving decision-making accuracy for critical choices without proportionally increasing overall computational complexity.
Data Source
AI summary
A system and method are provided and include a subject vehicle having vehicle actuation systems and vehicle sensors. A planning system includes a global route planner module, an inference module, a motion planner module, and a trajectory follower module. The inference module receives a route from the global route planner module and dynamic obstacles data from a perception system and determines a total cost for different sets of motions associated with different trajectories for traveling along the received route. The total cost includes an inferred cost based on a probability of the associated set of motions having an increased or decreased cost based on the dynamic obstacles data. The motion planner selects a particular set of motions based on the total costs and generates a smooth trajectory for the vehicle. The trajectory follower module controls the vehicle actuation systems based on the smooth trajectory.