AI in Automotive for Reliable Autonomous Driving
Introduction
The continuous advancement of connected vehicles is a priority for automotive Original Equipment Manufacturers (OEMs). These vehicles generate large amounts of data from the edge, which needs to be filtered and analyzed. AI techniques can identify anomalies in this data, pinpointing areas for improvement. A McKinsey report predicts that autonomous driving could generate $300–400 billion in revenue by 2034. To capitalize on this opportunity, automotive electronics must evolve in terms of reliability and functionality, driven by data from a fleet of connected vehicles.
Excelfore, a leader in connected mobility and a member of several key industry organizations, offers an edge-based AI solution that selectively transmits only useful data from vehicles to AWS, enabling real-time analysis and decision-making.
Industry Challenge
Another McKinsey report titled Unlocking the full life-cycle value from connected-car data highlights OEM efforts to provide end-to-end access to 1 to 2 terabytes of data per car each day. This is to enable continuous product and service improvements. We maintain that it is simply not reasonable to expect to upload the 1–2 terabytes of data per day. This is 2–3 orders of magnitude more than today’s top-level cellular data plans, which provide 60 or 100 gigabytes of data per device per month. In an environment where it is not feasible to send all the vehicle data to the cloud, the challenge is in determining what data to be sent to the cloud. The challenge specifically is in identifying what data is required to provide maximum value for reliability and functionality improvements.
Solution Overview
The following five-step process shows how to collect, filter, analyze, and use the vehicle data for AI-powered anomaly detection, creating a comprehensive closed-loop system for automotive intelligence.
1. eSync Data Pipeline
eSync provides the foundation for over-the-air (OTA) updates, data gathering, and transmission to the cloud.
2. eDatX Data Filtration
eDatX filters and aggregates data, reducing it by 99.9%, enabling only the most relevant data to be uploaded.
3. eDatX Data Visualization and Analysis
eDatX AWS component stores and visualizes the data collected from the vehicle fleet to analyze the performance of key automotive systems.
4. Cloud-based AI Modelling of Fleet-scale Data
Excelfore AI Engine on AWS utilizes the collected data from eDatX and trains a model to recognize normal data patterns.
5. Edge-based AI Anomaly Detection
AI models are pushed to the vehicle for real-time anomaly detection, with additional data uploaded for analysis when necessary.
Solution Architecture
The solution operates across two distinct layers: the vehicle layer, which includes components running in the vehicle, and the AWS Cloud layer, which encompasses services and resources deployed on AWS.
In-Vehicle
The service employs multiple filtering techniques to optimize data transmission to AWS Cloud. Time-based filtering reduces data frequency through temporal sampling—for example, selecting one sample per second from data generated every 10ms. Statistical filtering applies basic calculations to data prior to transmission, such as computing mean values from multiple sources or averaging readings over time. With logical filtering, the service transmits data only when specific conditions are met, such as when values exceed defined thresholds or when representing minimum or maximum readings. You can implement these filtering methods individually or combine them to achieve optimal data reduction based on your specific requirements.
The edge AI anomaly detection service enhances vehicle predictive maintenance and safety capabilities through advanced in-vehicle processing. The service receives periodic AI model updates pushed from AWS Cloud through OTA updates, ensuring that data filtration and selection processes leverage the latest AI learning. Using new generation automotive systems-on-chip (SoCs), the service’s AI engine performs transformer-based anomaly detection to monitor real-time data streams. This sophisticated monitoring identifies not only individual anomalies but also detects when data patterns across multiple sources deviate from expected behaviors, even if individual values remain within normal ranges. When the service detects pattern deviations, it automatically triggers bulk uploads, sending detailed data snapshots to AWS Cloud for comprehensive analysis.
On AWS
In each step, there is an aspect for training the AI model.
1. Data Ingestion
Edge AI use AWS IoT Core to receive data from the vehicle fleet. This step ensures that data is accessible for subsequent processing and analysis.
2. Data Storage
The data is stored in Amazon Timestream database, and in Amazon Simple Storage Service (Amazon S3) bucket for extract, transform, load (ETL) and analytics.
3. ETL Process
Following data ingestion and storage, Amazon Kinesis Data Streams and Amazon Data Firehose transform and prepare the data for analysis. To improve model training and analytics, the data undergoes structuring and cleansing. Presto on Amazon EMR makes it possible for domain experts to query and perform analytics on big data.
4. Model Training
Amazon SageMaker plays a pivotal role in training a model of expected vehicle data and their relationships, using the recent data collected from the entire fleet. Individual vehicles then use this model to find anomalous data relations.
5. Data Visualization
To facilitate insights into fleet performance and anomaly trends collected from the edge, Amazon QuickSight visualization tool is utilized. QuickSight reports help stakeholders to derive actionable insights from the processed data, promoting data-driven decision-making.
Key Benefits of Edge AI-Based Anomaly Detection
Selective data filtering and AI-powered anomaly detection provide the following advantages:
1. Reduced Data Transmission
By filtering data, the system ensures only the most relevant information is uploaded to the cloud, reducing data costs.
2. Predictive Maintenance
The edge AI engine detects anomalies early, helping to identify potential issues and prevent vehicle failures.
3. Real-Time Learning
The continuous loop of collecting, analyzing, and learning from vehicle data ensures ongoing system improvement.
4. Scalability
Excelfore’s cloud-based infrastructure can scale to support millions of vehicles, managing increasing data volumes effectively.
Conclusion
Managing high-value vehicle data is essential for continuous fleet improvement, but the massive volume of data generated daily poses significant challenges. This blog outlined how Excelfore’s solution addresses these challenges by using eDatX+AI for selective data filtration. The AI-powered anomaly detection ensures only relevant data is uploaded, reducing data volume by 99.9%.
This solution creates a dynamic learning loop, helping OEMs enhance vehicle performance and reliability through real-time analysis. With the integration of Excelfore’s eSync OTA solution, the process of data collection, analysis, and improvement becomes seamless, ensuring continuous fleet optimization. As the automotive industry moves toward more advanced, software-defined vehicles, this AI-based solution will play a crucial role.
Challenges In Automotive Software Development are becoming increasingly complex, but with the right data management solutions like Excelfore’s, automotive OEMs can stay ahead of the curve in building smarter, safer, and more reliable vehicles.
For more information on how AI can optimize automotive data management, visit Excelfore’s website or reach out to Cloudastra for AI-driven data solutions in automotive technology.
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