Vehicle Traffic Behavior Prediction Using Segmented Models

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Solution Overview

Problem

Autonomous vehicles face challenges in predicting the future maneuvering of objects in their environment, particularly when the traffic behavior of these objects does not meet a confidence threshold or deviates from the predominating traffic behavior of a like population, leading to unpredictability and potential safety issues.

Innovation Solution

The implementation of traffic behavior models that use perception and planning/decision-making modules to evaluate environmental information, identify objects, and predict their future maneuvering by extrapolating the predominating traffic behavior of reference objects, providing user assistance through alerts, training, and corrective operations to match the predominating driving behavior.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If the autonomous operation system relies solely on extrapolating predominating traffic behavior of reference objects, then the predictability of traffic flow is improved, but the system fails to accurately predict future maneuvering when objects deviate from typical behavior

Engineering Contradiction:

Improvepredictability of traffic flowVSAvoidability to handle atypical traffic behavior

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system segments traffic behavior prediction into two distinct components: (1) extrapolation of predominating traffic behavior for typical scenarios, and (2) detection and extrapolation of atypical behavior when deviations are identified. This segmentation allows the system to maintain high predictability for common patterns while adapting to unusual situations through separate detection mechanisms.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically switches between two prediction modes based on real-time analysis: using predominating behavior extrapolation when traffic follows normal patterns, and switching to atypical behavior detection and extrapolation when deviations are identified. This dynamic adaptation resolves the contradiction by making the system flexible enough to handle both typical and atypical scenarios appropriately.

Inventive Principle:

Principle #15Dynamics

2Measurement precision

If the system switches to extrapolating atypical traffic behavior when deviations are detected, then the accuracy of predicting future maneuvering is improved, but the complexity of the prediction system increases

Engineering Contradiction:

Improveaccuracy of predicting future maneuveringVSAvoidcomplexity of prediction system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system applies partial action by not continuously monitoring for atypical behavior in all scenarios, but only activating the more complex atypical behavior detection and extrapolation mechanisms when actual deviations are identified. This approach achieves high accuracy when needed while avoiding the constant computational overhead and complexity of maintaining both systems at full capacity simultaneously.

Inventive Principle:

Principle #16Partial or excessive action

3Reliability

If the autonomous vehicle provides extensive user assistance through alerts and training, then the safety and user understanding is improved, but the loss of time for corrective operations increases

Engineering Contradiction:

Improvesafety and user understandingVSAvoidtime for corrective operations

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system provides preliminary user assistance by issuing alerts and training about atypical traffic behavior in advance, before corrective operations are needed. This preliminary education equips users with the knowledge to handle similar situations more efficiently in the future, reducing the time required for actual corrective operations while maintaining high safety standards through proactive information delivery.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS10147324B1Providing user assistance in a vehicle based on traffic behavior models

Publication Date: 2018.12.04 TOYOTA JIDOSHA KK

AI summary

Providing user assistance in a vehicle includes evaluating information about an environment surrounding the vehicle, including identifying an object in the environment surrounding the vehicle, and predicting, based on the evaluation of the information about the environment surrounding the vehicle, the future maneuvering of the object. The user assistance further includes receiving a traffic behavior model that describes a predominating traffic behavior of a like population of reference objects. The prediction includes switching from extrapolating the predominating traffic behavior of the like population of reference objects, to, in response to identifying a traffic behavior of the object, extrapolating the traffic behavior of the object.