Vehicle ADAS Simulation for Driver-Specific Risk Adaptation
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Solution Overview
Problem
Existing vehicle technologies lack an effective method to evaluate and adapt vehicle parameters to reduce the likelihood of adverse events, such as accidents, and to determine the optimal installation of Advanced Driver Assistance Systems (ADAS) based on driver profiles and environmental conditions.
Innovation Solution
A system and method that utilize a simulation model to predict the risk of adverse events by simulating the vehicle's behavior with installed ADAS under various environmental conditions and driver profiles. The system adapts vehicle parameters, such as ADAS selection and vehicle settings, to minimize the risk of adverse events and computes insurance premiums based on the simulated risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a simulation model is used to predict and adapt vehicle parameters, then the likelihood of adverse events is reduced, but the device complexity increases
Solution Approach 1:
The system performs simulations in advance to predict adverse events and determine optimal vehicle parameters before actual driving occurs. The simulation model evaluates multiple scenarios and pre-determines the safest configuration, allowing the vehicle to be proactively prepared rather than reactively adjusted during driving.
Solution Approach 2:
The system creates a virtual copy of the vehicle and driving environment in the simulation model. This digital twin allows for risk-free testing of various parameter configurations and ADAS installations to determine the optimal setup without affecting the actual vehicle or requiring physical testing of multiple configurations.
2Reliability
If vehicle parameters are adapted for each driver and environment, then road safety is improved, but the ease of operation decreases
Solution Approach 1:
The system automatically performs the complex task of analyzing driver profiles, environmental conditions, and simulation results to determine optimal vehicle parameters. The adaptation process is self-executing without requiring manual intervention from the driver, making the complex safety optimization transparent and easy to use while still achieving personalized safety configurations.
Solution Approach 2:
The system dynamically adjusts vehicle parameters based on simulated results, driver profiles, and environmental conditions. By automatically changing parameters such as ADAS sensitivity, warning thresholds, and system activation levels, the system tailors the vehicle behavior to each specific driving scenario without requiring manual configuration by the user.
3Measurement precision
If advanced simulation models are implemented, then measurement precision of risk assessment is improved, but the use of energy increases
Solution Approach 1:
The system performs simulations to a sufficient level of detail needed for accurate risk assessment without unnecessarily exceeding that requirement. The simulation model incorporates only the necessary environmental factors, driver profile elements, and vehicle parameters relevant to safety, avoiding waste of computational resources on excessive detail while maintaining measurement precision.
Solution Approach 2:
The simulation model is divided into modular components that can be independently executed and optimized. By segmenting the complex simulation into separate modules (driver profile analysis, environmental modeling, vehicle dynamics, risk calculation), the system can process each segment efficiently and reduce overall energy consumption while maintaining comprehensive risk assessment accuracy.
Data Source
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AI summary
There is provided a system for adapting parameters of a vehicle for reduction of likelihood of an adverse event, comprising: hardware processor(s) executing a code for: performing, for each respective driver of multiple drivers: obtaining an indication of a vehicle driven by the respective driver, obtaining an indication of a certain advanced driver assistance system (ADAS) selected from multiple ADAS for installation in the vehicle, obtaining an environmental profile indicative of a prediction of an environment in which the vehicle with installed ADAS is predicted for driving therein at a future time interval, defining a simulation model in which the vehicle with installed ADAS is driving according to the environment profile, computing a risk of an adverse event during the future time interval by executing the simulation model, and selecting parameter(s) of the vehicle for adaptation thereof according to a predicted likelihood of reducing the risk of the adverse event.