Mobile Object Trajectory Control With Distance-Based Risk Areas
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Solution Overview
Problem
Existing technologies for mobile object control inefficiently utilize information of targets located near the object, particularly in generating target trajectories due to varying distances and collision risks.
Innovation Solution
A mobile object control device and method that sets different types of risk areas in distance-specific zones around the object, using a processor to generate a target trajectory by minimizing risk values through an arc model, considering collision probabilities and distances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If uniform cost is set for all obstacles in the image information, then the target trajectory can be generated using simple rules, but the efficiency of utilizing nearby target information is poor
Solution Approach 1:
The patent applies local quality by setting different cost values for different spatial regions. Specifically, obstacles within a predetermined distance from the mobile object are assigned a first cost value, while obstacles beyond this distance are assigned a second cost value. This spatial differentiation allows the system to efficiently prioritize nearby targets that require immediate attention while maintaining simple processing for distant objects, thereby resolving the contradiction between simplicity and efficiency.
2Reliability
If all targets in the image are considered for trajectory generation, then comprehensive safety is achieved, but the processing complexity and computational load increase
Solution Approach 1:
The patent segments the operational space into distinct zones based on distance from the mobile object. By dividing the environment into a near zone (within predetermined distance) and a far zone (beyond predetermined distance), the system can apply different processing strategies to each segment. This segmentation maintains comprehensive safety by considering all targets while reducing processing complexity through differentiated handling of near and far obstacles.
Solution Approach 2:
Different cost values are assigned to different spatial regions, creating local quality variations in the trajectory generation process. Nearby obstacles receive higher priority (first cost value) requiring more careful avoidance, while distant obstacles receive lower priority (second cost value). This local differentiation reduces overall computational complexity while maintaining comprehensive safety coverage.
Data Source
AI summary
A mobile object control device includes a storage medium storing computer-readable instructions and a processor connected to the storage medium. The processor executes the computer-readable instructions to recognize a surrounding situation of a mobile object, set a risk area to be avoided in a traveling process of the mobile object in a plurality of distance-specific areas centered on the mobile object on the basis of the recognized surrounding situation, generate a target trajectory indicating a route along which the mobile object is to travel in the future on the basis of the set risk area, and cause the mobile object to travel along the generated target trajectory. The processor sets different types of risk areas in accordance with the plurality of distance-specific areas.