XTRACTIS for ADAS & Autonomous Vehicle
Identification of the Longitudinal Action Required when Approaching Traffic Lights
Benchmark vs. Logistic Regression, Random Forest, Boosted Tree & Neural Network
Design an AI-based decision system that accurately diagnoses driving situations from a few vehicle parameters and from traffic light color, to select instantly the appropriate longitudinal action in a rational and explainable way.
Goals & benefits
- 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.
XTRACTIS-INDUCED DECISION SYSTEM
- The top-model is a decision system composed of 8 gradual rules without chaining, aggregated into 5 disjonctive fuzzy rules.
- Each rule uses from 1 to 3 predictors among the 4 variables that XTRACTIS identified as significant (out of the 5 potential predictors characterizing driving situations).
- Only a few rules are triggered at a time to compute the decision.
It has a perfect Real Performance (on unknown data).
It computes real-time predictions up to 70,000 decisions/second, offline or online (API).
BENCHMARK SCORES
LoR=Logistic Regression
RFo=Random Forest
BT=Boosted Tree
NN=Neural Network
Detailed results and explanations in full document
Use Case 2025/06 (v4.0)
Powered by XTRACTIS® REVEAL 13.0.45933 (2023/07)
CONTENTS
- Problem Definition
- XTRACTIS-induced Decision System
- XTRACTIS Process
- Top-Model Induction
- Explained Predictions for 5 unkown cases
- Top-Models Benchmark
- Quantitative Metrics
