TuSimple, Inc. (20240288868). DATA-DRIVEN PREDICTION-BASED SYSTEM AND METHOD FOR TRAJECTORY PLANNING OF AUTONOMOUS VEHICLES simplified abstract

DATA-DRIVEN PREDICTION-BASED SYSTEM AND METHOD FOR TRAJECTORY PLANNING OF AUTONOMOUS VEHICLES

Organization Name

TuSimple, Inc.

Inventor(s)

Xing Sun of San Diego CA (US)

Wutu Lin of San Diego CA (US)

Liu Liu of San Diego CA (US)

Kai-Chieh Ma of San Diego CA (US)

Zijie Xuan of San Diego CA (US)

Yufei Zhao of San Diego CA (US)

DATA-DRIVEN PREDICTION-BASED SYSTEM AND METHOD FOR TRAJECTORY PLANNING OF AUTONOMOUS VEHICLES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240288868 titled 'DATA-DRIVEN PREDICTION-BASED SYSTEM AND METHOD FOR TRAJECTORY PLANNING OF AUTONOMOUS VEHICLES

Simplified Explanation: The patent application describes a system and method for trajectory planning of autonomous vehicles based on data-driven predictions. It involves generating suggested trajectories, predicting trajectories of nearby agents, scoring trajectories, and outputting the best trajectory for the autonomous vehicle.

• Key Features and Innovation:

   - Data-driven prediction-based system for trajectory planning of autonomous vehicles.
   - Generation of suggested trajectories for autonomous vehicles.
   - Prediction of resulting trajectories of proximate agents using a prediction module.
   - Scoring of suggested trajectories based on predicted resulting trajectories of proximate agents.
   - Outputting the best trajectory for the autonomous vehicle based on the scoring mechanism.

• Potential Applications:

   - Autonomous driving systems.
   - Traffic management systems.
   - Fleet management systems.
   - Robotics and automation.

• Problems Solved:

   - Improved trajectory planning for autonomous vehicles.
   - Enhanced safety and efficiency in autonomous driving.
   - Better coordination with proximate agents on the road.

• Benefits:

   - Increased safety on the roads.
   - Optimal trajectory planning for efficient driving.
   - Reduced risk of collisions and accidents.
   - Enhanced overall performance of autonomous vehicles.

• Commercial Applications:

   - Autonomous vehicle manufacturers.
   - Transportation companies.
   - Smart city infrastructure developers.

• Prior Art:

   - Prior research on trajectory planning for autonomous vehicles.
   - Studies on prediction-based systems for autonomous driving.

• Frequently Updated Research:

   - Ongoing advancements in data-driven prediction models for autonomous vehicles.
   - Research on real-time trajectory planning algorithms for autonomous driving.

Questions about trajectory planning for autonomous vehicles:

1. How does the data-driven prediction system improve trajectory planning for autonomous vehicles?

   - The data-driven prediction system enhances trajectory planning by generating suggested trajectories based on predicted resulting trajectories of proximate agents, leading to safer and more efficient driving decisions.

2. What are the potential applications of this trajectory planning technology beyond autonomous vehicles?

   - This technology can also be applied in traffic management systems, fleet management, and robotics for improved coordination and efficiency.

Original Abstract Submitted

a data-driven prediction-based system and method for trajectory planning of autonomous vehicles are disclosed. a particular embodiment includes: generating a first suggested trajectory for an autonomous vehicle; generating predicted resulting trajectories of proximate agents using a prediction module; scoring the first suggested trajectory based on the predicted resulting trajectories of the proximate agents; generating a second suggested trajectory for the autonomous vehicle and generating corresponding predicted resulting trajectories of proximate agents, if the score of the first suggested trajectory is below a minimum acceptable threshold; and outputting a suggested trajectory for the autonomous vehicle wherein the score corresponding to the suggested trajectory is at or above the minimum acceptable threshold.