Autonomous Vehicle Motion Planning Using Object Cost Prediction
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Solution Overview
Problem
Autonomous vehicles face challenges in efficiently and safely navigating around proximate objects, such as vehicles and pedestrians, due to limitations in real-time motion planning and collision avoidance systems.
Innovation Solution
An autonomous vehicle system that processes sensor data to identify objects of interest, generates cost data for adjusting its motion to follow or lead these objects, and determines a motion plan to optimize its trajectory based on predicted paths and constraints, allowing it to proactively control its motion and avoid collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous vehicle uses traditional reactive motion planning systems, then collision avoidance is achieved, but the system cannot proactively optimize its trajectory and responds with delay to dynamic environmental changes
Solution Approach 1:
The system performs preliminary action by predicting future positions and trajectories of proximate objects before collisions or critical events occur. The cost function evaluation proactively assesses potential motion paths, allowing the autonomous vehicle to plan and execute optimal trajectories in advance rather than reacting to immediate threats, thereby reducing response delay while maintaining collision avoidance reliability.
2Reliability
If the autonomous vehicle implements comprehensive real-time motion planning for all proximate objects, then navigation safety is improved, but computational complexity and processing requirements increase significantly
Solution Approach 1:
The system applies local quality by focusing computational resources on evaluating motion costs specifically for proximate objects that pose potential risks or are relevant to the autonomous vehicle's trajectory. Rather than uniformly processing all objects in the environment, the cost function selectively evaluates objects based on their proximity, predicted paths, and potential impact on navigation safety, thereby reducing overall computational complexity while maintaining comprehensive safety monitoring.
3Reliability
If the autonomous vehicle maintains large distance from proximate objects for safety, then collision risk is reduced, but navigation efficiency and travel time increase
Solution Approach 1:
The system implements dynamics by making the safety distance adaptive rather than fixed. The cost function dynamically adjusts the optimal distance from proximate objects based on real-time factors including relative velocities, predicted trajectories, object types, and environmental context. This allows the autonomous vehicle to maintain larger distances when risks are higher and reduce distances when safety permits, thereby optimizing navigation efficiency without compromising collision risk reduction.
4Speed
If the autonomous vehicle uses simple motion planning algorithms, then computational speed is maintained, but the ability to handle complex environments and predict object behaviors is insufficient
Solution Approach 1:
The system replaces complex mechanical or iterative motion planning algorithms with a direct cost function evaluation approach. Instead of using computationally intensive simulations or repeated trial-and-error optimizations, the system directly calculates motion costs based on predicted object positions, trajectories, and predefined cost parameters. This substitution maintains high computational speed while achieving sophisticated environmental handling capability through the mathematical formulation of the cost function.
Data Source
AI summary
Systems and methods for controlling the motion of an autonomous are provided. In one example embodiment, a computer implemented method includes obtaining, by one or more computing devices on-board an autonomous vehicle, data associated with one or more objects that are proximate to the autonomous vehicle. The data includes a predicted path of each respective object. The method includes identifying at least one object as an object of interest based at least in part on the data associated with the object of interest. The method includes generating cost data associated with the object of interest. The method includes determining a motion plan for the autonomous vehicle based at least in part on the cost data associated with the object of interest. The method includes providing data indicative of the motion plan to one or more vehicle control systems to implement the motion plan for the autonomous vehicle.