Zoox, Inc. (20240208546). PREDICTIVE MODELS FOR AUTONOMOUS VEHICLES BASED ON OBJECT INTERACTIONS simplified abstract
Contents
PREDICTIVE MODELS FOR AUTONOMOUS VEHICLES BASED ON OBJECT INTERACTIONS
Organization Name
Inventor(s)
Ethan Miller Pronovost of Redwood City CA (US)
PREDICTIVE MODELS FOR AUTONOMOUS VEHICLES BASED ON OBJECT INTERACTIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240208546 titled 'PREDICTIVE MODELS FOR AUTONOMOUS VEHICLES BASED ON OBJECT INTERACTIONS
- Simplified Explanation:**
The patent application discusses techniques for training and executing machine learning prediction models to control autonomous vehicles in driving environments. These models are configured to output joint trajectory predictions for multiple objects by evaluating their interactions.
- Key Features and Innovation:**
- Training ML prediction models based on accuracy of predicted trajectories and agent-to-agent interactions. - Using a standard loss function and auxiliary loss to train the models effectively. - Determining auxiliary loss through classification models in a generative adversarial network (GAN) and divergence loss based on alternative ML prediction models.
- Potential Applications:**
- Autonomous vehicle navigation and control systems. - Traffic management and optimization in smart cities. - Robotics and drone technology for object interaction prediction.
- Problems Solved:**
- Enhancing the accuracy of trajectory predictions in complex environments. - Improving object interaction understanding for autonomous vehicles. - Increasing the reliability and safety of autonomous driving systems.
- Benefits:**
- Enhanced predictive capabilities for autonomous vehicles. - Improved decision-making in dynamic driving scenarios. - Increased safety and efficiency in transportation systems.
- Commercial Applications:**
Title: Advanced Machine Learning for Autonomous Vehicle Control This technology can be applied in: - Autonomous vehicle industry for self-driving cars. - Transportation and logistics companies for fleet management. - Smart city infrastructure for traffic optimization.
- Questions about Advanced Machine Learning for Autonomous Vehicle Control:**
1. How does this technology improve the safety of autonomous vehicles in complex driving environments? 2. What are the potential implications of using generative adversarial networks (GANs) in training ML prediction models for autonomous vehicles?
- Frequently Updated Research:**
Stay updated on advancements in machine learning algorithms for autonomous vehicle control and object interaction prediction. Explore the latest developments in GANs and divergence loss techniques for training ML models in dynamic environments.
Original Abstract Submitted
techniques are discussed herein for training and executing machine learning (ml) prediction models used to control autonomous vehicles in driving environments. in various examples, ml prediction models configured to output joint trajectory predictions for multiple objects in an environment may be trained by evaluating the interactions between the objects represented by the predicted trajectories. a training component may train an ml prediction model using a standard loss function based on the accuracy of the predicted trajectories relative to the ground truth trajectories, and based on an auxiliary loss determined by the agent-to-agent interactions represented by the predicted trajectories. the auxiliary loss may be determined by various techniques, including using a classification model trained to receive and classify sets of object trajectories in a generative adversarial network (gan), and/or determining a divergence loss based on an alternative ml prediction model that masks object interactions, thereby increasing reliance on object interactions in the training of the ml prediction model.
