Parking Lot Intersection Traversal Using Probabilistic Collision Risk
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Solution Overview
Problem
Autonomous vehicles face challenges in determining whether an intersection in a parking lot is traversable, especially in the absence of a traffic signal system, due to the complexity of navigating through crowded spaces with dynamic and static obstacles.
Innovation Solution
A probability model-based collision risk assessment method is employed, which involves obtaining driving route information and risk group data, setting a risk assessment zone, deriving occupancy probabilities, calculating collision risks, and determining whether the vehicle can traverse the intersection based on these assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous vehicle slows down or stops at intersections to avoid collisions, then collision risk is reduced, but navigation efficiency and traversal speed deteriorate
Solution Approach 1:
The system dynamically adjusts vehicle speed and traversal decisions based on real-time collision risk assessment. The probability model continuously evaluates occupancy probabilities of risk groups and calculates collision risks, enabling the vehicle to optimize its speed and traversal timing dynamically rather than following fixed stop-and-go patterns
Solution Approach 2:
The system changes the parameter of traversal decision from binary (stop/go) to probabilistic (risk threshold-based). By calculating collision risk probabilities and comparing them against thresholds, the system enables nuanced decision-making that balances safety and efficiency based on quantitative risk parameters
2Productivity
If the autonomous vehicle drives quickly through intersections without thorough assessment, then navigation efficiency is improved, but collision risk increases
Solution Approach 1:
The system performs preliminary collision risk assessment before the vehicle enters the intersection. By deriving occupancy probabilities and calculating collision risks in advance using the probability model, the system prepares traversal decisions beforehand, enabling efficient execution without last-minute braking or stopping
3Reliability
If the autonomous vehicle uses conservative driving strategies with frequent stops, then collision risk is reduced, but traversal time and energy consumption increase
Solution Approach 1:
The system uses feedback from the probability model's continuous assessment of occupancy probabilities and collision risks to adjust traversal timing. This feedback mechanism enables the vehicle to traverse intersections during low-risk windows identified by the model, reducing unnecessary stops while maintaining safety through real-time risk monitoring
Data Source
AI summary
A method of determining whether an autonomous vehicle is allowed to traverse at an intersection in a parking lot, includes obtaining, in response to the vehicle approaching the intersection, driving route information of the vehicle and information on a risk group, setting a risk assessment zone within the intersection, deriving an occupancy probability of the risk group, determining a collision risk between the vehicle and the risk group by use of the risk assessment zone and the occupancy probability, and determining, based on the collision risk, whether the vehicle is allowed to traverse the intersection.