Autonomous Vehicle Trajectory Prediction for Dynamic Obstacle Conflicts
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Solution Overview
Problem
Conventional autonomous vehicle control systems fail to accurately predict collisions with dynamic obstacles, particularly other vehicles, due to the lack of consideration for interaction between the vehicle and proximate dynamic vehicles, leading to suboptimal behavior and potential collisions.
Innovation Solution
A prediction-based system and method for trajectory planning that utilizes sensors to collect perception data, a computing device, and a trajectory prediction module to anticipate the reactions of other vehicles and dynamic objects, generating a predicted trajectory to avoid collisions by analyzing real-world traffic data and machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional polynomial-based trajectory planning is used, then the system is simple to implement, but it cannot accurately predict collisions with dynamic obstacles
Solution Approach 1:
The system performs preliminary prediction of future positions and reactions of dynamic obstacles before finalizing the trajectory plan. The trajectory prediction module anticipates where other vehicles and objects will be in the future, allowing the polynomial trajectory planner to account for these predicted positions rather than reacting to current positions only.
Solution Approach 2:
A trajectory prediction module is introduced as an intermediary component between the sensor data and the polynomial trajectory planner. This module processes raw sensor data about dynamic obstacles and generates predicted trajectory information that the polynomial planner can use, bridging the gap between simple polynomial mathematics and complex dynamic obstacle behavior.
2Reliability
If the system considers interaction with proximate dynamic vehicles, then collision prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The trajectory prediction module focuses computational resources on predicting the behavior of only those dynamic obstacles that are proximate to the autonomous vehicle and relevant to the current trajectory planning. Rather than modeling all objects in the environment, the system applies prediction specifically to nearby vehicles and objects that could potentially interact with the autonomous vehicle's planned path.
3Productivity
If polynomial expressions are used for spatial information, then mathematical computation is straightforward, but dynamic obstacle behavior cannot be accurately modeled
Solution Approach 1:
The trajectory planning process is segmented into distinct functional modules: a trajectory prediction module that handles dynamic obstacle behavior modeling, and a polynomial trajectory planner that generates the actual path. This segmentation allows each module to specialize - the prediction module focuses on accuracy while the polynomial planner maintains computational efficiency.
Data Source
AI summary
A prediction-based system and method for trajectory planning of autonomous vehicles are disclosed. A particular embodiment is configured to: receive training data and ground truth data from a training data collection system, the training data including perception data and context data corresponding to human driving behaviors; perform a training phase for training a trajectory prediction module using the training data; receive perception data associated with a host vehicle; and perform an operational phase for extracting host vehicle feature data and proximate vehicle context data from the perception data, generating a proposed trajectory for the host vehicle, using the trained trajectory prediction module to generate predicted trajectories for each of one or more proximate vehicles near the host vehicle based on the proposed host vehicle trajectory, determining if the proposed trajectory for the host vehicle will conflict with any of the predicted trajectories of the proximate vehicles, and modifying the proposed trajectory for the host vehicle until conflicts are eliminated.