Occluded Region Occupancy Reasoning for Autonomous Vehicle Control
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Solution Overview
Problem
Conventional autonomous vehicle navigation techniques often result in overly cautious behavior, leading to frequent braking and passenger discomfort due to the inability to accurately assess occluded regions in the environment, which can be either static or dynamic, causing uncertainty about the vehicle's actions.
Innovation Solution
An occlusion reasoning system that uses sensor data and map information to determine the likelihood of occluded regions being unoccupied by dynamic objects, classifying them as 'pseudo-visible' based on observation over time, allowing the vehicle to make more informed decisions and traverse the environment more efficiently and safely.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional autonomous vehicle navigation techniques are used to ensure safety, then collision risk is reduced, but vehicle mobility and passenger comfort deteriorate due to frequent unnecessary braking
Solution Approach 1:
The system performs preliminary assessment of occluded regions by analyzing visible entry regions and predicting object trajectories before the vehicle reaches the occluded area. This allows the vehicle to make informed decisions about whether to brake or continue moving, reducing unnecessary braking while maintaining safety.
Solution Approach 2:
The system introduces an intermediary reasoning layer that acts as a mediator between sensor data and navigation decisions. This occlusion reasoning system analyzes the probability of object presence in occluded regions and provides nuanced information that enables more sophisticated decision-making, balancing safety and mobility.
2Reliability
If conventional autonomous vehicle navigation techniques are used to ensure safety, then collision risk is reduced, but passenger comfort and system responsiveness worsen due to delayed decision-making
Solution Approach 1:
The system performs preliminary analysis of occluded regions and object trajectories in advance, before the vehicle needs to make critical navigation decisions. This head-start approach reduces decision-making delay by having assessment results ready when needed, improving both responsiveness and passenger comfort.
Solution Approach 2:
The system dynamically adjusts its assessment based on real-time conditions, object velocities, and distances. By making the reasoning process adaptive rather than static, the system can provide faster assessments when conditions are favorable while maintaining thorough analysis when needed, reducing overall decision-making time.
3Reliability
If conventional autonomous vehicle navigation techniques are used, then safety is maintained through conservative behavior, but system complexity and computational resources worsen due to extensive processing requirements
Solution Approach 1:
The system extracts and focuses only on the most critical aspects of occlusion reasoning - analyzing entry regions, predicting trajectories, and assessing probabilities. By taking out only the essential analysis components rather than processing all possible data, the system reduces computational complexity while maintaining safety.
Solution Approach 2:
The system applies different levels of analysis to different regions based on their importance. Entry regions and areas with high probability of object presence receive more detailed analysis, while low-risk areas receive simpler assessment. This localized quality approach reduces overall processing requirements while maintaining safety where it matters most.
Data Source
AI summary
Techniques are discussed for controlling a vehicle, such as an autonomous vehicle, based on occluded areas in an environment. An occluded area can represent areas where sensors of the vehicle are unable to sense portions of the environment due to obstruction by another object or sensor limitation. An occluded region for an object is determined, along with one or more visible regions proximate the occluded region. Entry and/or exit regions may be determined based on known directions of traffic and/or drivable surface boundaries. Based on a threshold speed, the vehicle can designate portions of the occluded region as pseudo-visible. Additionally, if a dynamic object traverses through the occluded region from the entry region, a portion of the occluded region may be considered pseudo-visible. Pseudo-visibility may also be determined based on movement of an occluded area. The vehicle can be controlled to traverse the environment based on the pseudo-visibility.