Intersection Collision Avoidance Using Intended Path Prediction
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Solution Overview
Problem
The complexity of processing large volumes of transportation network data from various sources, particularly at intersections, leads to latency and inconsistencies in predicting future behavior, resulting in unneeded notifications and challenges in collision avoidance for ego vehicles.
Innovation Solution
A system utilizing a processor that determines intended paths for ego vehicles based on turn signals and destination settings using high-definition maps, and transmits these paths to a conflict detection module to identify potential collisions with other road users, employing a shared world model and conflict detection module for accurate collision prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large volumes of transportation network data from various sources are processed to predict future behavior at intersections, then collision avoidance accuracy is improved, but processing latency increases and notifications become inconsistent
Solution Approach 1:
The patent segments the path prediction process into two distinct modules: a turn signal-based path prediction module and a destination-based path prediction module. Each module processes specific data sources independently and generates predictions that are later integrated. This segmentation allows parallel processing of different data streams, reducing overall processing latency while maintaining comprehensive collision avoidance accuracy through the combination of multiple prediction sources.
2Measurement precision
If multiple data sources (turn signals, destination settings) are integrated for path prediction, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system divides the complex path prediction task into two separate prediction modules: one handling turn signal data and another handling destination setting data. Each module has a dedicated function and processes its specific data type, which simplifies the internal logic of each module while achieving comprehensive prediction accuracy through their integration. This modular approach reduces system complexity by avoiding a single monolithic complex system.
Solution Approach 2:
The patent introduces a conflict detection module as an intermediary that receives and integrates predictions from both the turn signal-based module and the destination-based module. This intermediary component harmonizes the outputs from multiple data sources, resolving potential conflicts and producing a unified path prediction. The intermediary structure manages the complexity of integrating multiple data sources by providing a standardized interface and coordination mechanism.
3Speed
If turn signal data is used to generate intended path, then real-time responsiveness is improved, but reliability decreases when turn signal is not determined
Solution Approach 1:
The conflict detection module serves as an intermediary that receives path predictions from both the turn signal-based module and the destination-based module. When turn signal data is available, it provides real-time responsive predictions. When turn signal data is not determined, the intermediary seamlessly integrates destination-based predictions to maintain reliability. This intermediary structure ensures continuous reliable path prediction by switching between or combining sources based on data availability.
Solution Approach 2:
The system implements a universal path prediction framework that can handle multiple data sources (turn signals, destination settings) through a single integrated architecture. The conflict detection module is designed to work with either or both data sources, making the system multi-functional and adaptable. This universality ensures that the system maintains reliability across different operating conditions by being able to process and integrate predictions from any available data source.
Data Source
AI summary
An intersection collision avoidance system determines, for an ego vehicle, a direction indicated by its turn signal, its destination setting, or both, generates, where the direction is determined, a possible intended path relative to an intersection using a high-definition (HD) map and the direction, and generates, where the destination setting is determined, a possible intended path for using the HD map and the destination setting. Where the direction and the destination setting are both determined, the direction indicated by the turn signal is compared to the possible intended path generated using the destination setting, and one of the possible intended paths is selected based on the comparison. The system transmits, to a conflict detection module, a set of drive goals for the ego vehicle relative to the intersection that conforms to the intended path. The module can determine a possible collision with another road user using the drive goals.