Autonomous Vehicle Social Driving Style Learning Framework

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Solution Overview

Problem

Autonomous vehicles face challenges in adapting to local driving styles, which can lead to inefficiencies or increased risk due to preconfigured rules that fail to account for dynamic social norms and environmental conditions, such as aggressive or cautious driving behaviors in different areas.

Innovation Solution

A social driving style learning framework that allows autonomous vehicles to perceive and adopt driving styles by transmitting and aggregating driving style elements with a centralized server or through peer-to-peer communication over a wireless network, enabling them to adjust their behavior to match surrounding vehicles and maintain smooth traffic flow.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If preconfigured driving rules are used in autonomous vehicles, then the vehicle operation is simple and reliable, but the vehicle cannot adapt to local driving styles and dynamic social norms

Engineering Contradiction:

Improveadaptability to local driving stylesVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements dynamic adaptability by enabling autonomous vehicles to learn and adjust their driving styles in real-time based on local conditions. The system transitions from static preconfigured rules to dynamic style adaptation through continuous observation of surrounding vehicles and environmental factors, allowing the vehicle to modify its behavior dynamically while maintaining operational reliability

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The autonomous vehicle performs self-learning of driving styles by independently observing and analyzing the behavior of surrounding vehicles and environmental conditions. The system autonomously determines appropriate driving styles without requiring external configuration or intervention, enabling the vehicle to serve itself in adapting to local driving norms

Inventive Principle:

Principle #25Self-service

2Productivity

If aggressive driving style is adopted in urban areas, then trip time is reduced, but accident risk increases due to frequent lane cuts by other vehicles

Engineering Contradiction:

Improvetrip efficiencyVSAvoidsafety

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system continuously monitors and observes the driving behaviors of surrounding vehicles in real-time, using this feedback to dynamically adjust its own driving style. By detecting aggressive lane-cutting behaviors from other vehicles, the autonomous vehicle can adaptively modify its acceleration and lane-changing strategies to maintain both efficiency and safety according to actual traffic conditions

Inventive Principle:

Principle #23Feedback

3Reliability

If defensive driving strategy is used in suburban areas, then safety is improved, but trip completion time increases significantly

Engineering Contradiction:

ImprovesafetyVSAvoidtrip time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The autonomous vehicle dynamically adjusts its driving aggressiveness based on the observed driving styles of surrounding vehicles in suburban areas. Rather than maintaining a fixed defensive strategy, the system learns the local driving norms and adapts its behavior accordingly, enabling it to achieve both safety and reasonable trip efficiency by matching the defensive but not overly cautious style of surrounding traffic

Inventive Principle:

Principle #15Dynamics

4Adaptability or versatility

If preconfigured driving rules are used, then system reliability is maintained, but the vehicle cannot respond to dynamic weather conditions and environmental changes

Engineering Contradiction:

Improveresponse to environmental conditionsVSAvoidcomplexity of environmental perception

Core Design Contradiction:

Adaptability or versatilityVSDifficulty of detecting and measuring

Solution Approach 1:

The autonomous vehicle independently perceives and analyzes environmental conditions including weather, road conditions, and traffic patterns, then autonomously determines appropriate driving style adjustments. The system self-services by integrating multiple sensor inputs and automatically adapting its behavior without requiring external guidance or complex preconfigured rule sets for every possible environmental scenario

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS11238733B2Group driving style learning framework for autonomous vehicles

Publication Date: 2022.02.01 BAIDU USA LLC

AI summary

A social driving style learning framework or system for autonomous vehicles is utilized, which can dynamically learn the social driving styles from surrounding vehicles and adopt the driving style as needed. Each of the autonomous vehicles within a particular driving area is equipped with the driving style learning system to perceive the driving behaviors of the surrounding vehicles to derive a set of driving style elements. Each autonomous vehicle transmits the driving style elements to a centralized remote server. The server aggregates the driving style elements collected from the autonomous vehicles to determine a driving style corresponding to that particular driving area. The server transmits the driving style back to each of the autonomous vehicles. The autonomous vehicles can then decide whether to adopt the driving style, for example, to follow the traffic flow with the rest of the vehicles nearby.