Language is one of the most important tools that we have in our toolbox – we read, write, debug, think, and communicate, in the provided language terms. The Measurable Scenario Description Language (M-SDL) is a highly developed modeling language addressing the challenges of Autonomous Vehicle (AV) and Advanced Driver Assistance Systems (ADAS) verification.
Language is one of the most important tools that we have in our toolbox – we read, write, debug, think, and communicate, in the provided language terms. The Measurable Scenario Description Language (M-SDL) is a highly developed modeling language addressing the challenges of Autonomous Vehicle (AV) and Advanced Driver Assistance Systems (ADAS) verification.
The challenge: The growing complexity of automated driving systems such as Lane Keep Assist, Lane Centering and Adaptive Cruise Control challenge existing verification and validation (V&V) methodologies used in the automotive industry. As these systems become more prevalent, bugs surface and failures occur.
Whether you are working on ADAS and about to move up to more advanced autonomous functions, or working on the long tail of L4 autonomy, one thing is for sure: you are about to face exponential growth in the number of scenarios you need to test in order to get your autonomous product out.
Language is one of the most important tools that we have in our toolbox – we read, write, debug, think, and communicate, in the provided language terms. The Measurable Scenario Description Language (M-SDL) is a highly developed modeling language addressing the challenges of Autonomous Vehicle (AV) and Advanced Driver Assistance Systems (ADAS) verification.
The challenge: The growing complexity of automated driving systems such as Lane Keep Assist, Lane Centering and Adaptive Cruise Control challenge existing verification and validation (V&V) methodologies used in the automotive industry. As these systems become more prevalent, bugs surface and failures occur.
Whether you are working on ADAS and about to move up to more advanced autonomous functions, or working on the long tail of L4 autonomy, one thing is for sure: you are about to face exponential growth in the number of scenarios you need to test in order to get your autonomous product out.
Increasingly, thought leaders and executives within the automotive industry have shared their concerns about the validity of miles and disengagements as metrics for safe deployment of autonomous vehicles, and some have started proposing alternative metrics.
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