18158424. AUTONOMOUS VEHICLE PREDICTION LAYER TRAINING simplified abstract (GM Cruise Holdings LLC)
Contents
AUTONOMOUS VEHICLE PREDICTION LAYER TRAINING
Organization Name
Inventor(s)
Thanard Kurutach of Carrboro NC (US)
Ariel Arturo Perez Chavez of Mountain View CA (US)
AUTONOMOUS VEHICLE PREDICTION LAYER TRAINING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18158424 titled 'AUTONOMOUS VEHICLE PREDICTION LAYER TRAINING
The present disclosure pertains to autonomous vehicle (AV) training, specifically AV prediction layer training. The process involves receiving road data representing a real-world environment encountered by an AV and using a prediction layer to generate a predicted trajectory of a target vehicle based on the road data.
- Prediction layer training for autonomous vehicles
- Generating predicted trajectories of target vehicles using road data
- Calculating distance metrics for predicted trajectories
- Updating prediction layers based on distance metrics
- Improving AV performance and safety through predictive modeling
Potential Applications: - Autonomous driving systems - Traffic management and optimization - Fleet management for transportation companies - Advanced driver assistance systems - Research and development in the automotive industry
Problems Solved: - Enhancing the accuracy of predicting vehicle trajectories - Improving the safety and efficiency of autonomous vehicles - Optimizing decision-making processes for AVs in complex environments
Benefits: - Increased safety on the roads - Enhanced efficiency in transportation systems - Reduced traffic congestion and emissions - Improved overall driving experience for passengers
Commercial Applications: Title: Autonomous Vehicle Prediction Layer Training for Enhanced Safety and Efficiency This technology can be utilized by companies developing autonomous vehicles, transportation agencies, and automotive manufacturers looking to enhance the performance and safety of their vehicles in real-world environments.
Prior Art: Readers interested in exploring prior art related to AV prediction layer training can start by researching machine learning algorithms for autonomous vehicles, predictive modeling in transportation systems, and advancements in sensor technology for AVs.
Frequently Updated Research: Researchers in the field of autonomous vehicles are constantly exploring new algorithms and techniques to improve the accuracy and efficiency of predictive modeling for AVs. Stay updated on recent developments in machine learning, computer vision, and sensor fusion technologies for autonomous driving systems.
Questions about AV Prediction Layer Training: 1. How does AV prediction layer training contribute to the overall safety of autonomous vehicles? - AV prediction layer training enhances safety by accurately predicting the trajectories of target vehicles, allowing AVs to make informed decisions and avoid potential collisions.
2. What are the key challenges in developing and implementing prediction layers for autonomous vehicles? - Challenges include handling complex real-world environments, ensuring real-time processing of data, and optimizing prediction algorithms for varying driving conditions.
Original Abstract Submitted
The present disclosure generally relates to autonomous vehicle (AV) training and, more specifically, to AV prediction layer training. In some aspects, the present disclosure provides a process for receiving road data representing a real-world environment encountered by an AV and generating, using a prediction layer of the AV, a predicted trajectory of a target vehicle, wherein the predicted trajectory comprises one or more waypoints and wherein the predicted trajectory is based on the road data. In some aspects, the process can further include steps for calculating a distance metric for the predicted trajectory, wherein the distance metric is based on a distance between the one or more waypoints and one or more corresponding drivable areas and updating the prediction layer of the AV based on the distance metric. Systems and machine-readable media are also provided.
