18176781. ITERATIVE TRAJECTORY REPLANNING FOR EMERGENCY OBSTACLE AVOIDANCE simplified abstract (TOYOTA JIDOSHA KABUSHIKI KAISHA)

ITERATIVE TRAJECTORY REPLANNING FOR EMERGENCY OBSTACLE AVOIDANCE

Organization Name

TOYOTA JIDOSHA KABUSHIKI KAISHA

Inventor(s)

James A. Dallas of San Jose CA (US)

Michael Thompson of San Juan Capistrano CA (US)

Yan Ming Goh of Palo Alto CA (US)

Avinash Balachandran of Sunnyvale CA (US)

ITERATIVE TRAJECTORY REPLANNING FOR EMERGENCY OBSTACLE AVOIDANCE - A simplified explanation of the abstract

This abstract first appeared for US patent application 18176781 titled 'ITERATIVE TRAJECTORY REPLANNING FOR EMERGENCY OBSTACLE AVOIDANCE

Simplified Explanation

The abstract describes systems and methods for trajectory planning for an autonomous vehicle, including determining a first trajectory plan, computing an optimal sequence for a second spatial location, and calculating a second trajectory plan by updating the first trajectory plan with information from the computed optimal sequence.

• Autonomous vehicle trajectory planning system:

   * Determines initial trajectory plan for vehicle.
   * Computes optimal sequence for subsequent spatial location.
   * Updates initial trajectory plan with optimal sequence information to calculate final trajectory plan.

Potential Applications

This technology can be applied in various industries such as:

• Autonomous vehicles
• Robotics
• Transportation logistics

Problems Solved

The technology addresses the following issues:

• Efficient trajectory planning for autonomous vehicles
• Optimal route selection
• Real-time adjustments for changing conditions

Benefits

The benefits of this technology include:

• Improved safety
• Enhanced efficiency
• Reduced travel time
• Increased accuracy in trajectory planning

Potential Commercial Applications

Optimizing Trajectory Planning for Autonomous Vehicles: Improving Efficiency and Safety

Unanswered Questions

How does this technology handle unexpected obstacles in the vehicle's path?

The system may need additional sensors or algorithms to detect and avoid obstacles in real-time.

What kind of computational power is required to implement this trajectory planning system?

The computational requirements may vary depending on the complexity of the environment and the speed at which trajectory adjustments need to be made.

Original Abstract Submitted

Systems and methods of trajectory planning for an autonomous vehicle are disclosed. Exemplary implementations may: determine a first trajectory plan for the vehicle traveling along a first spatial location at a first point in time, the first trajectory plan being a reference trajectory plan; compute an optimal sequence for the vehicle traveling along a second spatial location at a second point in time subsequent the first point in time; and calculate a second trajectory plan for the vehicle by updating the first trajectory plan with information from the computed optimal sequence.