18158226. TRAJECTORY PREDICTION THROUGH SEMANTIC INTERACTION simplified abstract (GM Cruise Holdings LLC)
Contents
TRAJECTORY PREDICTION THROUGH SEMANTIC INTERACTION
Organization Name
Inventor(s)
Abbas Shikari of New York NY (US)
Prathyush Katukojwala of Pasadena CA (US)
TRAJECTORY PREDICTION THROUGH SEMANTIC INTERACTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18158226 titled 'TRAJECTORY PREDICTION THROUGH SEMANTIC INTERACTION
The abstract of the patent application discusses technology related to predicting trajectories of agents in an autonomous vehicle environment based on semantic interactions between the agents and AVs.
- Access raw data of an AV operating in an environment.
- Access an interaction model that models semantic interactions between agents in driving environments.
- Identify a probability distribution of various semantic interactions of an agent with respect to the AV in the environment.
- Predict different trajectories of the agent in the environment based on the probability distribution of the various semantic interactions.
Potential Applications: - Autonomous driving systems - Traffic management systems - Collision avoidance systems
Problems Solved: - Predicting trajectories of agents in complex driving environments - Enhancing safety in autonomous vehicle operations - Improving efficiency of traffic flow
Benefits: - Increased safety on the roads - Enhanced efficiency in traffic management - Reduced risk of accidents
Commercial Applications: Predictive analytics for autonomous vehicles: Enhancing safety and efficiency in transportation systems.
Questions about Predicting Trajectories of Agents in an Autonomous Vehicle Environment:
1. How does the technology predict different trajectories of agents in the environment?
- The technology utilizes an interaction model to identify semantic interactions between agents and AVs, allowing for the prediction of various trajectories based on probability distributions.
2. What are the potential applications of this predictive technology in autonomous driving systems?
- The technology can be applied in autonomous driving systems, traffic management systems, and collision avoidance systems to enhance safety and efficiency on the roads.
Original Abstract Submitted
Aspects of the subject technology relate to systems, methods, and computer-readable media for predicting trajectories of agents in an autonomous vehicle (“AV”) environment based on semantic interactions between the agents and AVs. Raw data of an AV operating in an environment can be accessed. An interaction model that models semantic interactions between agents in driving environments can be accessed. A probability distribution of various semantic interactions of an agent with respect to the AV in the environment can be identified through application of the interaction model. Different trajectories of the agent in the environment can be predicted according to the probability distribution of the various semantic interactions of the agent with respect to the AV through application of the interaction model.
