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AI in automotive Industry – Artificial Intelligence and machine learning (ML) are empowering automakers to cash in on the abundance of data amassed but not currently used effectively.
From self-driving cars, connected cars, electric vehicles to driver and vehicle health monitoring systems, AI and ML have sunk their roots deep into the mobility ecosystem.
Advanced driver-assistance and monitoring systems (ADAS & DMS), the emergence of in-vehicle sensing platforms, and government regulations contribute to AI-based driver and passenger monitoring systems (DMS).
Using in-seat sensors and in-car cameras, automakers are calling on machine and deep learning algorithms to build complex driver monitoring systems that analyze driving patterns and cognitive behavioral functions like drowsiness and alcohol-induced inebriation detection and security facets like biometric authentication.
AI is also applied in the crucial aspect of range prediction. In addition to driving behavior attributes like acceleration, braking, and average speeds, battery management systems, in Electric Vehicles (EVs), are influenced by ambient variables like temperature and humidity as well as charging and discharging cycles.
Just like in healthcare an area we know well at NeuTigers, advanced automotive health monitoring platforms are using AI to diagnose vehicles in real-time. Digital Twins of drivetrain components are used to generate predictive insights for overheating, battery charging, and fuel-air charge composition.
Comprehensive reports on fuel efficiency-pilferage, driving performance, routes-schedules, and performance of emissions control systems are made available with minimal manual intervention. Utilizing AI to predict possible malfunctions reduces downtime and curtails the risk of mission-critical breakdowns.
Interior camera systems are increasingly present in car cockpits to interpret gestures and monitor drivers and passengers. Yet automakers struggle with the training of deep neural network models.
NeuTigers framework is able to automate the generation of compact and accurate DNNs so that models can be deployed on compact in vehicle computing devices.
How do you protect the vehicle’s Controller Area Network (CAN)?
Today’s connected cars are targets of attacks, and securing the network is paramount. Traditional Intrusion Detection systems for the automotive industry aren’t enough.
NeuTigers Shark AI framework goes beyond existing models.
Our innovative AI solution detects unknown system vulnerabilities, manages associated vulnerabilities, and improves incident response when these vulnerabilities are exploited.
Our unique Cyber Security AI solution extracts intelligence from known real-world CPS/IoT attacks, representing them in the form of regular expressions, and uses machine learning techniques on this ensemble of regular expressions to generate new attack vectors and security vulnerabilities.
Before any model training can take place, sensors’ data needs to be denoised and labeled. NeuTigers data stewardship is key to ML model’s accurately.
It is often expensive and complicated to recruit a large pool of subjects to train a model properly. Moreover, it is often essential to increase real-world data to maximize models’ accuracy.
NeuTigers Synthetic Data generation engine – SynthDeep, a solution whose accuracy was proven in healthcare solutions, helps automakers accelerate model training, even when only a small sample of real-world data is available.
Automakers who need to share sensitive data with their partners can safely communicate anonymized synthetic data.
NeuTigers grow and prune toolchain is a unique patented ML Model Optimization solution.
Models optimized with NeuTigers are energy-efficient, compact, and accurate for efficient edge deployment.
Once optimized with NeuTigers’ toolchain, binary models can be deployed on smaller, less expensive sensors and microchips.
