Road Agent Trajectory Prediction with Confidence Estimation
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Solution Overview
Problem
Current road-agent trajectory prediction systems fail to adequately capture the uncertainty of human actions, particularly in scenarios where road agents have multiple choices, such as intersections, leading to unreliable predictions.
Innovation Solution
The system employs multiple trajectory predictors, including probabilistic and physics-based models, and provides confidence estimates for predicted trajectories using deep neural networks, allowing for evaluation of trustworthiness over a temporal horizon, enabling more reliable predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current trajectory prediction systems are used, then the system is simple to operate, but the reliability of predictions is insufficient due to failure to account for uncertainty
Solution Approach 1:
The system segments the trajectory prediction task into multiple independent predictors (e.g., constant velocity predictor, constant acceleration predictor, lane-change predictor), each handling specific motion patterns. This segmentation allows the system to improve overall prediction reliability by combining specialized models while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system implements a universal prediction framework that handles multiple types of road agents (pedestrians, cyclists, vehicles) and multiple motion scenarios (straight movement, turning, lane changing) through a single integrated architecture. This multi-functionality approach improves reliability across diverse scenarios without proportionally increasing system complexity.
2Reliability
If multiple trajectory predictors are employed to account for uncertainty, then the reliability of predictions improves, but the device complexity increases
Solution Approach 1:
The system dynamically selects and weights different trajectory predictors based on the specific scenario and confidence levels. Rather than statically combining all predictors, the system adapts the prediction ensemble in real-time based on road agent behavior patterns, environmental conditions, and predictor performance metrics, thereby improving reliability while managing complexity through adaptive rather than exhaustive combination.
Solution Approach 2:
The system changes parameters such as prediction horizons, uncertainty thresholds, and predictor weighting factors based on situational context. By adjusting these parameters dynamically, the system optimizes prediction reliability for different scenarios without requiring a complete redesign of the predictor architecture for each case.
Data Source
AI summary
Systems and methods described herein relate to predicting a trajectory of a road agent external to a vehicle. One embodiment generates first and second predicted road-agent trajectories using respective first and second trajectory predictors based, at least in part, on a plurality of inputs including past road-agent trajectory information and vehicle sensor data; generates a confidence score for each predicted road-agent trajectory using a confidence estimator that includes a deep neural network, wherein generating the confidence scores includes computing them as a function of time within a predetermined temporal horizon; outputs the first and second predicted road-agent trajectories and their respective confidence scores; and controls operation of the vehicle based, at least in part, on one or more of the first predicted road-agent trajectory, the second predicted road-agent trajectory, the confidence score for the first predicted road-agent trajectory, and the confidence score for the second predicted road-agent trajectory.