Driving Assistance Maneuver Probability Distribution
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Solution Overview
Problem
Current autonomous driving systems face challenges in accurately predicting traffic scenarios and identifying maneuvers due to high computational demands, leading to potential safety and security issues.
Innovation Solution
A driving assistance method that observes road user states, assigns behavioral models, calculates maneuver and state distributions using a combination of previous probabilities and motion parameters, and determines collision risks to output warning signals or execute avoidance actions, leveraging both behavioral and dynamic models to reduce false detections and enhance reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If planning-based approaches are used to predict trajectories and intents of road users, then prediction accuracy is improved, but computational time and memory requirements increase
Solution Approach 1:
The prediction task is segmented into discrete maneuver types (e.g., lane keeping, lane changing, turning) with predefined motion parameters. Instead of computing continuous trajectory predictions, the system divides the problem into categorical maneuvers, each with associated probability distributions, reducing computational complexity while maintaining prediction accuracy.
Solution Approach 2:
Motion parameters and behavioral models are pre-computed and stored for different maneuver types. The system performs preliminary classification of road user intentions into discrete maneuvers, then applies pre-defined motion models to generate predictions, avoiding real-time computation of complex continuous models.
2Measurement precision
If planning-based approaches with cost functions are used to predict road user behavior, then behavior prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The system changes the parameter representation from continuous cost function evaluations to discrete maneuver probabilities. By parameterizing road user behavior as probability distributions over finite maneuver sets with associated motion parameters, the system simplifies the mathematical complexity while preserving predictive accuracy.
Solution Approach 2:
Instead of maintaining complex continuous cost function models, the system uses lightweight discrete maneuver categories with pre-computed motion parameters. These simplified representations are computationally inexpensive and can be rapidly updated or replaced based on observed behavior patterns.
3Reliability
If discrete maneuver probabilities with motion parameters are used, then false detections are reduced, but computational resources must be optimized
Solution Approach 1:
The system computes probability distributions only for a finite set of predefined maneuver types rather than evaluating all possible continuous trajectories. This partial action approach focuses computational resources on the most relevant maneuver categories, reducing overall computational resource usage while maintaining reliability through targeted probability calculations.
Data Source
AI summary
A driving assistance system includes a sensor set, a data storage device and an output device. The sensor set observes apparent states of each road user of the plurality of road users at successive time steps, the data processor assigns a behavioral model stored in the data storage device, the data processor calculating a new maneuver distribution that is a probability distribution over a finite plurality of alternative maneuvers and a new state distribution that is a probability distribution of possible states for each alternative maneuver of the finite plurality of alternative maneuvers. The data processor determines a risk of collision of the road vehicle with another road user, based on the new maneuver and state distributions of the target road user, and outputs one or more of a driver warning signal and executes an avoidance action if the risk of collision exceeds a predetermined threshold.