18125628. SYSTEM AND METHOD FOR EVALUATING MOTION PREDICTION MODELS simplified abstract (Mercedes-Benz Group AG)
Contents
SYSTEM AND METHOD FOR EVALUATING MOTION PREDICTION MODELS
Organization Name
Inventor(s)
Thomas Monninger of Sunnyvale CA (US)
Julian Schmidt of Stuttgart (DE)
Julian Jordan of Stuttgart (DE)
SYSTEM AND METHOD FOR EVALUATING MOTION PREDICTION MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18125628 titled 'SYSTEM AND METHOD FOR EVALUATING MOTION PREDICTION MODELS
Simplified Explanation: The patent application describes a computing system that can analyze motion prediction data from a vehicle to determine predicted trajectories for entities in the vehicle's surrounding environment.
Key Features and Innovation:
- Receiving motion prediction data from a vehicle
- Generating predicted trajectories for entities in the surrounding environment
- Evaluating prediction performance of the motion prediction model
- Matching predicted endpoints to underlying lanes
- Determining distances along lane segments
Potential Applications: This technology could be used in autonomous vehicles, traffic management systems, and road safety applications.
Problems Solved: This technology addresses the need for accurate motion prediction in dynamic environments to improve safety and efficiency on the roads.
Benefits:
- Enhanced safety for vehicles and pedestrians
- Improved traffic flow and congestion management
- Increased efficiency in autonomous driving systems
Commercial Applications: The technology could be applied in autonomous vehicle systems, smart city infrastructure, and transportation logistics for commercial fleets.
Prior Art: Readers can explore prior research in the fields of autonomous vehicles, machine learning, and traffic prediction models to understand the evolution of this technology.
Frequently Updated Research: Stay informed about advancements in machine learning algorithms, sensor technologies, and urban mobility studies related to motion prediction in autonomous vehicles.
Questions about Motion Prediction Technology: 1. How does this technology improve road safety? 2. What are the potential challenges in implementing this technology in real-world scenarios?
Question 1: How does this technology improve road safety? This technology enhances road safety by accurately predicting the trajectories of entities in the vehicle's surrounding environment, allowing for proactive decision-making to avoid potential collisions.
Question 2: What are the potential challenges in implementing this technology in real-world scenarios? Some challenges in implementing this technology include the need for robust data collection systems, integration with existing infrastructure, and addressing privacy concerns related to data sharing in connected environments.
Original Abstract Submitted
A computing system can receive motion prediction data from a vehicle, where the motion prediction data is generated by a motion prediction model executing on the vehicle. Based on the motion prediction data, the system can determine predicted trajectories for a plurality of entities in a surrounding environment of the vehicle. The system can evaluate a prediction performance of the motion prediction model by (i) matching, for each respective entity of the plurality of entities, a predicted endpoint of each predicted trajectory of the set of predicted trajectories to one or more underlying lanes of the road segment, (ii) matching a ground truth future position of the entity to one or more underlying lanes of the underlying lane topology, and (iii) determining a distance along one or more lane segments between the lane(s) matched to the predicted endpoint and the lane(s) matched to the ground truth future position.
