XTRACTIS FOR ADAS & AUTONOMOUS VEHICLE
Identification of the Longitudinal Action Required when Approaching Traffic Lights
Benchmark vs. Logistic Regression, Random Forests, Boosted Trees & Neural Networks
Design an AI-based decision system that efficiently diagnoses driving situations from a few vehicle parameters and traffic light color, to select rationally the appropriate longitudinal action.
Identify the car parameters involved in the driving situation diagnosis and rediscover
–or confirm– the causal relationships between specific parameters, their combination, and the requested action.
Help engineers design reliable intelligible autonomous vehicles that assist the driver efficiently. Intelligible means that the internal decision logic of the decision system is explicit; therefore this ADAS could be audited by the domain expert and certified by the regulator.
Enforce the use of stable and transparent models before embedding them in the vehicle.
We get a Predictive Model that is:
Intelligible.
The top-model is a decision system composed of 8 gradual rules without chaining; each rule uses some of the 4 variables that XTRACTIS identified as predictors.
Perfect Real Performance on External Test.
EFFICIENT & OPERATIONAL.
Running in real-time up to 70,000 predictions per second (i7 @2.5GHz with 8 physical cores), offline or online (API).
LoR=Logistic Regression
RFo=Random Forests
BT=Boosted Trees
NN=Neural Networks
Detailed explanations in full document.
Use Case 2023/07 (v2.1)
Results by
XTRACTIS® REVEAL 13.0.45933 (2023/10)
DOCUMENT CONTENTS
- Problem Definition
- XTRACTIS Solution
- Top-Model Induction
- Explained Predictions for 5 unkown cases
- Top-Models Benchmark
- Appendices
