Probabilistic Lane Tracking for Ambiguous Vehicle Path Transitions
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Solution Overview
Problem
Conventional methods for assigning lanes to objects in autonomous vehicles are unreliable due to measurement uncertainty, especially in complex environments like intersections and lane transitions, leading to inaccurate lane identification.
Innovation Solution
A probabilistic framework using an instantaneous lane classifier and Hidden Markov Model (HMM) to determine the lane occupied by an object, combining current state probabilities with historical data to track lane transitions accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional lane prediction methods are used based on heading, position, speed, and yaw rate, then the system can provide a predicted forward travel path, but the lane assignment becomes unreliable due to measurement uncertainty
Solution Approach 1:
The patent transitions from single-time-point lane prediction to multi-temporal lane tracking by incorporating historical lane assignments and transition probabilities over time. This temporal dimension allows the system to accumulate evidence and resolve ambiguities that plague single-frame predictions, particularly in complex scenarios like intersections and lane transitions.
Solution Approach 2:
The system implements feedback through the use of transition matrices that encode probabilities of lane changes based on object characteristics and environmental context. This feedback mechanism continuously refines lane assignments by comparing predicted trajectories with actual observed positions across multiple time steps, correcting errors and improving reliability.
2Measurement precision
If single-view object detection is used to identify lane position, then the system can process data quickly, but it becomes difficult to accurately identify the lane the object is following
Solution Approach 1:
The system performs preliminary actions by maintaining a history of lane assignments and transition probabilities before making final lane determination. Instead of relying solely on current single-view data, the system pre-computes and stores temporal patterns of lane usage, which are then applied to resolve ambiguous current-state observations.
Solution Approach 2:
The patent ensures continuity of useful action by continuously tracking objects across multiple frames and maintaining persistent lane assignment hypotheses. This continuous temporal observation compensates for the information loss in individual frames, allowing the system to infer lane membership even when single-view detection is ambiguous.
3Device complexity
If traditional prediction methods are used in intersections and merging lanes, then the system can maintain simple processing, but position and heading alone are not enough to accurately determine the route the object is following
Solution Approach 1:
The patent segments the lane assignment problem into discrete probabilistic states representing different lanes and possible transitions between them. By dividing the continuous navigation space into discrete lane segments with defined transition probabilities, the system manages complexity while improving reliability in complex scenarios like intersections and merging lanes.
Solution Approach 2:
The system changes parameters by transitioning from deterministic lane assignment to probabilistic lane tracking. Instead of committing to a single lane prediction based on current position and heading, the system maintains probability distributions over multiple possible lanes, updating these probabilities over time based on observed trajectories and transition models.
Data Source
AI summary
Systems and methods for assigning a lane to an object in an environment of an autonomous vehicle are disclosed. The methods include assigning an instantaneous probability to each of a plurality of lanes in the environment based on a current state of the object, generating a transition matrix for each of the plurality of lanes, and identifying the lane in which the object is moving at the current time t based on the instantaneous probability and the transition matrix. The instantaneous probability is a measure of likelihood that the object is in that lane at a current time. The transition matrix encodes one or more probabilities that the object transitioned either into that lane or out of that lane at the current time.