Autonomous Vehicle Occlusion Handling for Intersection Crossing
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Solution Overview
Problem
Autonomous vehicles face challenges in safely navigating intersections and turns due to sensor occlusions, where objects beyond the range of their sensors are undetectable, potentially leading to collisions.
Innovation Solution
The vehicle's computing devices identify potential occlusions by selecting hypothetical vehicle points beyond the sensor range, determining distances and travel times, and comparing these to thresholds to safely maneuver the vehicle, using detailed maps and sensor data to account for undetectable objects and adjust its path accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the vehicle relies solely on sensor data within its detection range, then the device complexity is reduced, but the reliability deteriorates due to undetectable objects beyond sensor range
Solution Approach 1:
The system performs preliminary actions by identifying potential occlusions and selecting hypothetical vehicle points beyond the sensor range before actual crossing maneuvers. It calculates travel times for these hypothetical vehicles in advance and compares them to threshold values, allowing the autonomous vehicle to proactively adjust its path to avoid potential collisions with undetectable objects.
2Reliability
If the vehicle extends detection beyond sensor range using hypothetical vehicles, then the reliability improves, but the loss of information increases due to assumptions about undetectable objects
Solution Approach 1:
The system introduces hypothetical vehicles as intermediary constructs to represent potential occluded objects beyond sensor range. These hypothetical vehicles serve as mediators between the known sensor data and the unknown occluded areas, allowing the system to reason about potential dangers without direct observation. The hypothetical vehicles are defined by trajectories, positions, and travel times that bridge the information gap in occluded zones.
3Reliability
If the vehicle calculates travel times for hypothetical vehicles to assess safety, then the reliability improves, but the loss of time increases due to additional computations
Solution Approach 1:
The system uses the vehicle's own maneuvering parameters (speed, acceleration, trajectory) to calculate travel times for hypothetical vehicles, rather than requiring external data or complex simulations. By leveraging already-known vehicle characteristics and simple kinematic calculations, the system performs self-service safety assessments that minimize computational overhead while maintaining reliability.
Data Source
AI summary
The technology relates to identifying sensor occlusions due to the limits of the ranges of a vehicle's sensors and using this information to maneuver the vehicle. As an example, the vehicle is maneuvered along a route that includes traveling on a first roadway and crossing over a lane of a second roadway. A trajectory is identified from the lane that will cross with the route during the crossing at a first point. A second point beyond a range of the vehicle's sensors is selected. The second point corresponds to a hypothetical vehicle moving towards the route along the lane. A distance between the first point and the second point is determined. An amount of time that it would take the hypothetical vehicle to travel the distance is determined and compared to a threshold amount of time. The vehicle is maneuvered based on the comparison to complete the crossing.