Probabilistic Validation Approach For Advanced Driver Assistance Systems.pdf

A Probabilistic Approach for Validation of Advanced Driver Assistance Systems (Delft University of Technology)

This technical report proposes a probabilistic approach to efficiently validate advanced driver assistance systems (ADAS), addressing challenges posed by the increasing complexity and safety demands of these systems.

Current Validation Methods:

The authors highlight limitations with conventional validation methods, which rely heavily on simulations and field tests:

Simulations: Inefficient due to the large number of scenarios required to cover all operating conditions.
Field Tests: Inconclusive as they cannot fully replicate real-world scenarios and conditions.

Proposed Solution:

The report introduces a methodology utilizing randomized algorithms (RAs), which offer:

Efficiency: Reduce the number of simulations needed for reliable performance estimation.
Comprehensiveness: Allow for representative sampling of operating conditions.

Steps in the Validation Methodology:

1. Define Perturbation Space and Performance Criteria: Identify potential disturbances (e.g., weather, traffic) and key performance indicators (KPIs) for the ADAS under validation (e.g., braking distance, collision avoidance).
2. Select Sample Size and Space: Determine an optimal number of samples and relevant scenarios based on desired confidence levels and expected variability.
3. Iterative Randomized Simulation: Run a series of simulations with randomized inputs from the defined perturbation space.
4. Hardware Validation: Validate the simulation models using a Vehicle-Hardware-in-the-Loop (VEHIL) facility, where the simulated ADAS controls a real vehicle in controlled conditions.

Case Study: Adaptive Cruise Control (ACC)

The report uses ACC as a case study to demonstrate the application of the probabilistic approach. It highlights:

RA Advantages: Faster convergence, ability to capture complex interactions, and improved robustness compared to traditional methods.
Challenges: Determining appropriate sample complexity and mitigating model uncertainty.

Recommendations and Future Research:

The authors propose solutions for addressing sample complexity issues and suggest future research directions, including:

Developing more sophisticated RAs tailored to specific ADAS characteristics.
Investigating the impact of model uncertainty on validation results.
* Exploring integration with other validation techniques for comprehensive testing.