Autonomous Vehicle Interaction Prediction for Real-Time Trajectory Planning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in accurately predicting the future locations of objects within their surrounding environment due to complex interactions with other objects, traffic rules, and their own motion, which can lead to safety and efficiency issues.
Innovation Solution
A computing system that uses sensors and machine-learned models to predict object trajectories by analyzing initial trajectories, interactions, and environmental data, including traffic rules, to generate probable interaction trajectories and inform motion planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the autonomous vehicle uses complex interaction prediction models to accurately predict object trajectories, then prediction accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The prediction system is divided into multiple independent modules: trajectory prediction module, interaction detection module, and motion planning module. Each module processes specific aspects of the prediction task separately, allowing parallel computation and reducing overall computational complexity while maintaining comprehensive prediction accuracy.
Solution Approach 2:
The system performs preliminary trajectory predictions for multiple objects before analyzing their interactions. By pre-computing individual object trajectories using sensor data and historical patterns, the system reduces the computational burden during the interaction analysis phase, as the foundation data is already prepared.
2Reliability
If the autonomous vehicle analyzes multiple potential trajectories and interactions, then safety improves, but processing time increases
Solution Approach 1:
The system dynamically adjusts the level of analysis based on situational context. In high-risk scenarios with multiple objects and potential interactions, the system performs comprehensive multi-trajectory analysis. In low-risk scenarios, it uses simplified prediction models, thereby maintaining safety when needed while reducing processing time when appropriate.
Solution Approach 2:
The system implements real-time processing with optimized algorithms that skip unnecessary computational steps. When object trajectories are clearly divergent and interaction risk is low, the system rapidly processes minimal necessary data, rushing through the analysis phase to maintain real-time responsiveness without compromising safety-critical scenarios.
3Reliability
If the autonomous vehicle uses sensor data and machine-learned models to predict interactions, then prediction reliability improves, but system complexity increases
Solution Approach 1:
The machine-learned models are designed to perform multiple functions: trajectory prediction, interaction detection, and risk assessment. This multi-functional approach consolidates what could be separate complex systems into unified models, improving prediction reliability across different scenarios while managing overall system complexity through reuse of the same core algorithms.
Data Source
AI summary
Systems and methods for determining object motion and controlling autonomous vehicles are provided. In one example embodiment, a computing system includes processor(s) and one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the processor(s) cause the computing system to perform operations. The operations include obtaining data associated with a first object and one or more second objects within a surrounding environment of an autonomous vehicle. The operations include determining an interaction between the first object and the one or more second objects based at least in part on the data. The operations include determining one or more predicted trajectories of the first object within the surrounding environment based at least in part on the interaction between the first object and the one or more second objects. The operations include outputting data indicative of the one or more predicted trajectories of the first object.