Adaptive Occupancy Grid Map for Vehicle Navigation
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Solution Overview
Problem
Conventional occupancy grid maps for vehicles are inefficient as they calculate and store unnecessary information, leading to increased computational and memory burdens, as they do not adapt to varying driving situations, such as speed and environment demands.
Innovation Solution
The occupancy grid map is dynamically adapted based on the vehicle's driving situation by adjusting cell size, density, and coordinate system, ensuring only relevant information is calculated and stored, with cell sizes varying according to speed and environment relevance, and sensor characteristics are optimized accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional occupancy grid maps calculate and store all environmental information uniformly, then complete environmental representation is achieved, but computational burden and memory usage increase significantly
Solution Approach 1:
The patent applies local quality by making cell sizes non-uniform across the occupancy grid map. Cells closer to the vehicle are smaller to capture detailed information in near vicinity, while cells farther away are larger to reduce memory requirements. This resolves the contradiction by providing high representation quality where needed (near the vehicle) while reducing computational burden in distant regions where fine detail is less critical for driving decisions.
Solution Approach 2:
The patent implements dynamics by adapting the occupancy grid map configuration to the vehicle's current driving situation, particularly speed. At higher speeds, the grid map uses larger cell sizes and reduced resolution to minimize processing time, while at lower speeds, finer resolution is maintained. This dynamic adaptation resolves the contradiction between complete environmental representation and computational burden by adjusting representation quality according to real-time operational requirements.
2Ease of manufacture
If uniform cell sizes are used in occupancy grid map, then implementation simplicity is maintained, but information efficiency decreases as unnecessary information is calculated
Solution Approach 1:
The patent makes cell sizes vary locally based on distance from the vehicle. Near-field cells are small to capture critical detailed information for immediate obstacles, while far-field cells are larger to reduce unnecessary calculation. This local differentiation resolves the contradiction by maintaining implementation simplicity through a systematic gradient approach while dramatically improving information efficiency by calculating only relevant detail levels at different distances.
3Measurement precision
If high resolution occupancy grid map is used, then environmental detail accuracy is improved, but memory requirements increase
Solution Approach 1:
The patent applies local quality by creating a distance-dependent cell size gradient where only near-field regions use small, high-resolution cells for accurate environmental detail, while far-field regions use larger cells that consume less memory. This resolves the contradiction by concentrating memory resources on the critical near-field environment where high precision is necessary for safe driving, while using coarser representation in distant regions where fine detail is less important.
Solution Approach 2:
The patent implements partial action by providing high-resolution environmental representation only in the partial region immediately surrounding the vehicle, rather than uniformly across the entire field of view. This selective approach resolves the contradiction between environmental detail accuracy and memory requirements by applying high precision only where it is actually needed for driving decisions, while using lower precision in peripheral regions.
4Productivity
If occupancy grid map is adapted to driving situation, then information optimization is achieved, but system complexity increases
Solution Approach 1:
The patent implements dynamics by making the occupancy grid map configuration adaptive to the vehicle's driving situation, particularly speed. The system dynamically adjusts cell sizes and resolution based on real-time speed measurements, using coarser grids at high speeds for faster processing and finer grids at low speeds for greater accuracy. This resolves the contradiction between information processing efficiency and system complexity by automating the adaptation process through straightforward speed-based rules rather than complex decision-making systems.
Solution Approach 2:
The patent applies parameter changes by systematically varying the occupancy grid map parameters (cell size, resolution, extent) based on the vehicle's speed parameter. This parameter-driven approach resolves the contradiction between information processing efficiency and system complexity by using a single controlling parameter (speed) to automatically adjust multiple grid map characteristics, avoiding the need for complex multi-parameter optimization systems.
Data Source
AI summary
An occupancy grid map for a vehicle includes several cells disposed in grid-like fashion. The cells of the occupancy grid map are adapted, as a function of a driving situation of the vehicle, to the driving situation. Areas of the cells are configured to be smaller in a region closer to the vehicle, and are configured to be larger in a region further away from the vehicle.