Predicting VRU Jaywalking via Segmented Heading Models
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Solution Overview
Problem
Autonomous vehicles face challenges in predicting and responding to the unpredictable movements of vulnerable road users (VRUs) such as cyclists and pedestrians, who often disregard traffic regulations and engage in irregular maneuvers, posing risks for safe navigation.
Innovation Solution
The development of machine-learning techniques and predictive models that analyze location and environmental data to predict VRU jaywalking behaviors and estimate their heading towards terminal locations, enabling autonomous vehicles to plan safe maneuvers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles use traditional detection and control systems to respond to VRUs, then the system complexity is reduced, but the safety and reliability deteriorate due to inability to predict unpredictable VRU movements
Solution Approach 1:
The system performs preliminary actions by training separate jaywalking prediction and heading estimation models offline before deployment. These models predict VRU future behaviors in advance, allowing the autonomous vehicle to plan safer maneuvers proactively rather than reactively, thereby improving safety without increasing real-time system complexity
Solution Approach 2:
The predictive modeling system is segmented into two independent models: a jaywalking prediction model that determines whether a VRU will cross the roadway, and a heading estimation model that predicts the VRU's destination. This segmentation allows independent training and optimization of each model, improving overall prediction accuracy while maintaining manageable system complexity
2Measurement precision
If a single integrated model is used to predict both jaywalking behavior and heading, then device complexity is reduced, but manufacturing precision and model accuracy deteriorate
Solution Approach 1:
The prediction system is divided into two specialized models: one for predicting whether a VRU will jaywalk and another for estimating the heading/destination. Each model is trained independently on specific aspects of VRU behavior, resulting in higher prediction accuracy for each task compared to a single general-purpose model
Solution Approach 2:
Each model serves its specific function independently without requiring the other model's infrastructure. The jaywalking prediction model independently determines crossing intent, while the heading estimation model independently predicts destination, allowing both to be optimized and trained separately for maximum precision
3Measurement precision
If comprehensive environmental data and multiple features are analyzed to predict VRU behavior, then prediction accuracy improves, but computational expense increases
Solution Approach 1:
Environmental features and data processing are performed in advance during the offline training phase. The models learn from pre-processed comprehensive environmental data including road geometry, intersections, and historical VRU behavior, so that during real-time operation, only lightweight inference is required, reducing computational expense while maintaining high prediction accuracy
Solution Approach 2:
The computational task is segmented into offline training (computationally intensive) and online inference (computationally light). During training, comprehensive environmental data is fully analyzed to build accurate models. During real-time operation, only the trained models are applied with minimal computational overhead, balancing accuracy and energy consumption
Data Source
AI summary
Jaywalking behaviors of vulnerable road users (VRUs) such as cyclists or pedestrians can be predicted. Location data is obtained that identifies a location of a VRU within a vicinity of a vehicle. Environmental data is obtained that describes an environment of the VRU, where the environmental data identifies a set of environmental features in the environment of the VRU. The system can determine a nominal heading of the VRU, and generate a set of predictive inputs that indicate, for each of at least a subset of the set of environmental features, a physical relationship between the VRU and the environmental feature. The physical relationship can be determined with respect to the nominal heading of the VRU and the location of the VRU. The set of predictive inputs can be processed with a heading estimation model to generate a predicted heading offset (e.g., a target heading offset) for the VRU.