Intersection Exit Prediction for Autonomous Vehicles
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately predicting the exit of a moving object from an intersection due to numerous possible paths influenced by traffic lights and other obstacles, making it difficult to determine the most likely exit.
Innovation Solution
A prediction system that uses an exit coordinate map and a predictive model to calculate probabilities based on the moving object's heading direction, speed, acceleration, and movement history, identifying the exit with the highest probability for the object to exit the intersection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple possible paths are considered for moving objects in an intersection, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The intersection area is segmented into multiple discrete regions, each corresponding to a potential exit path. The moving object's trajectory is analyzed by evaluating probabilities for each segmented region independently, allowing accurate multi-path prediction while managing computational complexity through structured division of the problem space.
Solution Approach 2:
Instead of exhaustively analyzing all possible paths with equal detail, the system applies partial action by focusing computational resources on the most probable exit regions based on the moving object's current state and historical data. This selective approach maintains high prediction accuracy for likely paths while reducing unnecessary computations for improbable scenarios.
2Reliability
If trajectory planning avoids all possible exits, then safety is improved, but navigation efficiency deteriorates
Solution Approach 1:
The system performs preliminary probability calculations for all possible exit paths before finalizing the trajectory plan. By pre-assessing the likelihood of each exit based on moving object characteristics and environmental factors, the autonomous vehicle can confidently select the most probable exit path, achieving both safety through comprehensive evaluation and efficiency by avoiding unnecessary consideration of highly improbable alternatives.
Solution Approach 2:
The system dynamically adjusts the probability thresholds and evaluation parameters based on the specific context, such as the type of moving object, traffic conditions, and intersection geometry. This parameter adaptation allows the system to balance safety and efficiency by being more conservative when uncertainties are high and more decisive when confidence is strong, optimizing navigation performance across different scenarios.
Data Source
AI summary
A moving object such as a vehicle is identified within an intersection having multiple exits. The moving object and the intersection and its exits may be identified based on sensor data obtained from various sensors mounted on an ADV. An exit coordinate map is generated based on the orientation of the moving object and a relative position of each of the exits of the intersection with respect to the current position of the moving object. For each of the exits, an exit probability of the exit that the moving object likely exits the intersection using the exit coordinate map. Thereafter, a trajectory of the ADV is planned to navigate through the intersection to avoid the collision with the moving object based on the exit probabilities of the exits of the intersection. The above process is iteratively performed for each of the moving objects detected within the proximity of the intersection.