Corridor Intent Prediction for Early Lane Change Response
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Solution Overview
Problem
Autonomous vehicles face challenges in predicting the lane-changing behavior of adjacent vehicles, which can lead to inadequate adjustment of driving behavior and potentially increase the risk of collisions.
Innovation Solution
A system and method for corridor intent prediction using neural networks, where map data and sensor data are used to define corridors and extract features of road agents and corridors, allowing the neural network to predict the future position of road agents and adjust driving behavior accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous vehicle waits to detect lane changes until they occur, then the response time is minimized, but the safety and driving experience deteriorate due to late adjustment
Solution Approach 1:
The system performs preliminary action by predicting lane change intent before the actual lane change occurs. The neural network analyzes current trajectory, position, and contextual features to forecast future lane change behavior, enabling the autonomous vehicle to adjust its driving behavior in advance rather than reacting after the lane change happens.
Solution Approach 2:
The system applies preliminary anti-action by preparing countermeasures before the harmful event (collision risk) occurs. By predicting lane change intent, the autonomous vehicle can proactively adjust its trajectory, speed, or lane position to prevent potential collisions, rather than reacting after the danger arises.
2Measurement precision
If the autonomous vehicle uses complex prediction models, then the prediction accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The system segments the prediction task into distinct feature extraction stages and processing steps. It separately extracts trajectory features, position features, and contextual features, then feeds them into the neural network. This segmentation allows for optimized processing of each feature type while maintaining overall prediction accuracy.
Solution Approach 2:
The system applies partial action by focusing computational resources on the most critical features for prediction. Rather than processing all possible data equally, it prioritizes extracting and analyzing key features such as trajectory, position, and immediate contextual information that have the highest impact on prediction accuracy.
Data Source
AI summary
A method includes generating a training data set comprising a plurality of training examples, wherein each training example is generated by receiving map data associated with a road portion, receiving sensor data associated with a road agent located on the road portion, defining one or more corridors associated with the road portion based on the map data and the sensor data, extracting a plurality of agent features associated with the road agent based on the sensor data, extracting a plurality of corridor features associated with each of the one or more corridors based on the sensor data, and for each corridor, labeling the training example based on the position of the road agent with respect to the corridor, and training a neural network using the training data set.