ADAS Risk Simulation for Vehicle Parameter Adaptation
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Solution Overview
Problem
Current vehicle technologies lack a systematic approach to evaluate the effectiveness of advanced driver assistance systems (ADAS) in reducing the likelihood of adverse events, such as accidents, and do not provide objective, quantitative methods to select the most effective technologies for specific driver and environmental conditions.
Innovation Solution
A system and method that utilize simulation models to predict the risk of adverse events by simulating vehicles with installed ADAS under predicted environmental conditions and driver profiles, allowing for the adaptation of vehicle parameters to reduce risk, and iteratively selecting parameters to achieve a defined threshold of safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simulation models are used to predict risk of adverse events, then objective quantitative evaluation of ADAS effectiveness is achieved, but computational complexity and time requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-defining multiple simulation models corresponding to different adverse event types (collisions, pedestrian accidents, etc.) and pre-establishing the evaluation framework before actual ADAS evaluation occurs. This allows the complex simulation infrastructure to be ready in advance, reducing real-time computational burden while maintaining high evaluation precision through pre-configured scenario libraries and baseline risk profiles.
2Reliability
If multiple simulation models are executed to evaluate different ADAS configurations, then comprehensive risk assessment is achieved, but computational time and resources increase
Solution Approach 1:
The system segments the comprehensive risk assessment into multiple independent simulation models, each dedicated to evaluating specific adverse event types (e.g., collision risk, pedestrian accident risk, lane departure risk). Each simulation model can be executed independently and in parallel, allowing the system to maintain comprehensive evaluation coverage while reducing overall computational time through parallel processing of segmented risk scenarios.
Solution Approach 2:
The system implements partial action by allowing users to select and execute only the simulation models relevant to specific evaluation needs. Rather than requiring all simulation models to run for every ADAS configuration evaluation, the system enables selective execution of subsets of simulations based on the particular ADAS being evaluated and the specific risk concerns, thereby reducing computational time while maintaining sufficient assessment reliability for the given context.
3Object-affected harmful factors
If vehicle parameters are adapted based on predicted risk reduction, then road safety is improved, but vehicle complexity and adaptation requirements increase
Solution Approach 1:
The system applies parameter changes by identifying specific vehicle parameters that, when adjusted, are most likely to reduce the predicted adverse event risk. Rather than requiring comprehensive adaptation of all vehicle systems, the system focuses on modifying key parameters (such as speed limits, acceleration profiles, steering assistance levels) that have the greatest impact on risk reduction for the specific ADAS configuration and driving conditions, thereby simplifying the adaptation process while effectively reducing harmful factors.
Data Source
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.