ADAS Calibration Using Crowdsourced Driving Preferences
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Solution Overview
Problem
Current advanced driving assistance systems (ADAS) lack the ability to adapt to individual driver preferences and dynamic traffic conditions, leading to potential discomfort and inefficiency, as they are typically calibrated at production and not adjusted online.
Innovation Solution
A method and system that utilize crowdsourced data to adapt ADAS calibration in real-time, considering both personal driver preferences and traffic dynamics, using crowd-local and crowd-global distributions to adjust vehicle control parameters such as braking distance and energy consumption without requiring labeled data, allowing for anonymous data processing to ensure privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ADAS is configured conservatively to increase safety, then safety is improved, but driver comfort deteriorates
Solution Approach 1:
The system dynamically adapts ADAS calibration parameters in real-time based on individual driver behavior patterns and current traffic conditions. Instead of using fixed conservative settings, the system continuously adjusts parameters such as following distance, braking force, and intervention thresholds to match the driver's preferences and the environmental context, thereby maintaining safety while improving comfort.
Solution Approach 2:
The system changes multiple calibration parameters simultaneously based on detected driver preferences and traffic dynamics. By analyzing driver behavior patterns (e.g., preferred following distance, braking style) and environmental factors (e.g., traffic density, road conditions), the system optimizes parameters like time gap, deceleration rate, and warning thresholds to achieve the right balance between safety and comfort for each situation.
2Ease of manufacture
If ADAS is calibrated at production, then manufacturing simplicity is maintained, but adaptability to individual drivers and conditions deteriorates
Solution Approach 1:
The system performs self-calibration by automatically analyzing the driver's behavior patterns and preferred driving style during normal operation. The driver无需进行复杂的手动校准操作，系统通过监测驾驶行为（如跟车距离、制动方式、转向习惯）自动学习并调整ADAS参数，从而在保持制造简单性的同时实现个性化适应。
Solution Approach 2:
The system continuously monitors driver responses to ADAS interventions and uses this feedback to refine calibration parameters. By tracking whether the driver accepts or overrides system suggestions, and analyzing behavioral patterns, the system iteratively optimizes the calibration to match individual preferences and local traffic conditions, enabling post-production adaptation without complex reconfiguration.
3Adaptability or versatility
If crowdsourced data is used for adaptation, then adaptability to traffic dynamics is improved, but data processing complexity increases
Solution Approach 1:
The system uses a unified crowdsourced traffic model that serves multiple ADAS functions simultaneously. The same aggregated traffic pattern data (e.g., typical speeds, following distances, congestion patterns for specific locations and times) is leveraged by multiple subsystems including adaptive cruise control, collision warning, and route planning, eliminating the need for separate data processing pipelines for each function and reducing overall system complexity.
Solution Approach 2:
The system merges individual driver calibration with aggregated crowdsourced traffic models into a unified adaptation framework. By combining personal preferences with anonymized collective behavior patterns from multiple vehicles in similar situations, the system creates a comprehensive calibration that benefits from both individual customization and collective intelligence, while sharing computational resources across different ADAS functions.
Data Source
AI summary
A vehicle includes an advanced driver-assistance system (ADAS) configured to intervene in an operation of the control system by complementing or overriding the driving input in response to detecting a driving condition dependent on a calibration parameter indicative of a preference of execution of the driving maneuver. The ADAS is calibrated based on a crowd-local distribution function of the calibration parameter indicative of a distribution of the preference of execution of the driving maneuver by other drivers of other vehicles at a specific location or a specific environment in response to detecting that the vehicle approaches the specific location or the specific environment.