In-Path Obstacle Detection for Lane-Keeping Vehicle Avoidance
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating environments with obstacles like tree limbs, foliage, and small objects on the road, as traditional sensor systems may incorrectly identify these as obstacles, leading to unsafe reactions or increased risk of damage.
Innovation Solution
The system employs advanced object detection and classification techniques to assess the proximity and type of objects, determining appropriate passing distances and operating parameters based on the vehicle's velocity, object dimensions, and position, allowing the vehicle to safely pass over or under obstacles without leaving its lane.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional sensor systems are used to detect objects in the environment, then the autonomous vehicle can identify potential obstacles, but the system may incorrectly identify objects like tree limbs, foliage, and small objects as obstacles, leading to unsafe reactions
Solution Approach 1:
The system changes the parameters of object assessment by evaluating multiple characteristics simultaneously (distance from surface, height above surface, position relative to vehicle path) rather than relying on a single detection threshold. This multi-parameter approach transforms how objects are classified, reducing false positives while maintaining safety.
Solution Approach 2:
The detection space is segmented into multiple regions (first pass region, second pass region, third pass region) with different safety thresholds and response criteria. Each region represents a different level of risk and requires different operational responses, allowing the system to differentiate between harmless objects and true obstacles more accurately.
2Reliability
If the autonomous vehicle maintains a conservative distance from all detected objects, then collision risk is reduced, but the vehicle's operational efficiency and productivity decrease
Solution Approach 1:
Different safety margins and operational parameters are applied locally based on the specific object characteristics and region. Instead of a uniform conservative distance from all objects, the system adjusts the passing distance and velocity thresholds according to the object's position, size, and type, allowing efficient navigation while maintaining safety where needed.
Solution Approach 2:
The system dynamically adjusts operational parameters (velocity, passing distance, response time) based on real-time object assessment. When objects are identified as low-risk (in later pass regions), the vehicle can operate more efficiently with smaller safety margins, while automatically increasing caution when objects require closer inspection or pose higher risk.
3Measurement precision
If the system uses multiple pass regions with different thresholds, then object assessment accuracy improves, but the device complexity increases
Solution Approach 1:
The detection environment is segmented into three distinct pass regions, each with defined thresholds for object distance and height. This segmentation provides a structured framework for assessment that improves precision while keeping the complexity manageable through clear regional boundaries and standardized evaluation criteria for each zone.
Data Source
AI summary
A vehicle can include various sensors to detect objects in an environment. In some cases, the object may be within a planned path of travel of the vehicle. In these cases, leaving the planned path may be dangerous to the passengers so the vehicle may, based on dimensions of the object, dimensions of the vehicle, and semantic information of the object, determine operational parameters associate with passing the object while maintaining a position within the planned path, if possible.