At a Glance
- Successful AI proof-of-concepts (PoCs) in mobility start with clear objectives and the selection of relevant data, ensuring alignment with business goals.
- Involving diverse teams across functions is key to navigating challenges and driving innovation through AI PoCs.
- Evaluating the scalability and business impact of a PoC is crucial before full-scale implementation, ensuring long-term success and measurable outcomes.
Choosing the right supplier who has adequate domain expertise for your AI-driven project requires a structured approach. Much like preparing a gourmet recipe – each step is vital for that perfect dish of success.
Here’s a step-by-step guide to execute your AI pilot project.
Data Strategy
Data Acquisition
A meticulous approach should be taken in gathering raw data that adheres rigorously to industry standards and specific project requirements. Your strategic partner should be able to identify the right data source, toolchain requirements, data module, and server requirements.
In the automotive sector, acquiring sensor data, Controller Area Network (CAN) data from various vehicle components, driver behaviour data, and environmental data is crucial. Ensuring this data is comprehensive, accurate, and in a format conducive to advanced AI analysis and predictive modelling is essential for effective AI deployment.
Data Exploration
During this stage, the primary goal is to gain a comprehensive understanding of the data while evaluating its quality and cleanliness. With the right statistical analysis and visualisation techniques, the strategic partner should be able to unearth valuable insights and discern patterns thus enabling you with data-driven decision-making. Analysing and visualising data to understand its key characteristics, uncover patterns, locate outliers, and identify relationships between variables.
For example, analysing data from Driver Monitoring Systems (DMS) and Cabin Monitoring Systems (CMS) can help identify patterns in driver fatigue, distraction, and other behavioural metrics. This understanding is crucial for developing effective AI solutions that enhance vehicle safety and driver comfort.
Data Pre-Processing
Data Quality is paramount. Through rigorous data pre-processing procedures (Data Cleaning, Data Integration, Data Packaging, Data Sampling), the strategic partner should ensure that the data is pristine, accurate, and primed for advanced analysis and modelling, enhancing the reliability of the AI-driven solutions for the next stage.
In automotive applications, debiasing data from various sensors, such as LIDAR, cameras, and radar, is essential. Normalising data from different sources ensures uniformity and comparability, which is crucial for accurate AI modelling. For instance, object detection systems for autonomous driving must process data from multiple sensors to provide a coherent understanding of the vehicle’s surroundings.
Data Confidentiality
In the automotive sector, data confidentiality is crucial for maintaining trust and safeguarding sensitive information. This encompasses protecting proprietary vehicle data and driver information. Maintaining confidentiality is not only a legal requirement, but its also a fundamental ethical practice, crucial for the integrity of automotive AI systems.
Furthermore, data anonymisation is key for confidentiality in automotive applications. It involves removing personal identifiers from driver and vehicle data, ensuring privacy while enabling AI to extract valuable insights. This process is vital for complying with privacy laws and maintaining trust and integrity.
Watch out for:
- Insufficient Data Quality and Quantity
AI models require high-quality, relevant data for training and validation. If the data used is incomplete, outdated, or biased, it can lead to inaccurate predictions and hinder the success of the AI deployment. Data Source and Data Collection mechanisms must be planned to keep the project timelines in mind.
- Data Drift
Data drift is the alteration of data distribution over time, affecting AI model accuracy. It happens when incoming data differs from training data, necessitating continuous monitoring and retraining to maintain AI system effectiveness.
- Compliance with Regulations
Adherence to legal frameworks such as GDPR is essential. These laws dictate stringent standards for data protection and confidentiality in automotive applications.
- Access Control and Security Protocols
Implement strict access controls and robust security measures. This includes encryption of data and ensuring only authorised personnel can access sensitive information, protecting against unauthorised use and potential cyber threats.
AI Strategy
Understanding Quantitative Objectives
In the initial phase, the emphasis lies on developing a profound comprehension of the distinct objectives within your company. This deep understanding ensures seamless alignment between AI solutions with the overarching business goals and creative vision. It’s imperative to define the acceptable range for precision and accuracy of the AI models from the outset, with the strategic partner evaluating their feasibility.
For instance, achieving high accuracy in object detection for autonomous driving systems is critical in automotive applications. The AI models must accurately detect and classify objects in various driving conditions, reducing the risk of accidents.
Watch out for:
- Lack of ROI Clarity
AI initiatives often require significant investments. If the expected return on investment (ROI) is unclear or not well-defined, it can be challenging to secure funding or implement the project at scale. A clear understanding of the expected ROI is to be agreed upon between the strategic partner and the customer before starting the project.
- Scope Creep
Expanding the scope of the AI project beyond the initial objectives can lead to increased costs, longer timelines, and a higher risk of failure. It’s essential to maintain a clear focus on the original goals.
Model Building, Testing, and Validation, Selection
AI experts meticulously construct model frameworks using advanced statistical procedures (Classification, Clustering, Regression, Association Analysis). Model training is carried out for these frameworks using training data sets. Error analysis and model validation ensure AI model fine-tuning and the right model selection from various candidate models for desired performance and accuracy.
The model-building phase must include validation with diverse automotive data, covering both known and new scenarios. For example, testing AI models for autonomous driving in different environments and traffic conditions ensures their robustness and reliability. Direct feedback from automotive experts aids in refining and selecting the most effective model.
Watch out for:
- Lack of Scalability and Integration
AI solutions must integrate seamlessly with existing systems and processes. If an AI model or system cannot integrate and scale effectively with the broader business environment, it may not be suitable for production.
- Model Drift
Model drift occurs when a previously well-performing AI model starts making less accurate predictions due to changes in data patterns or external factors. Addressing model drift involves constant monitoring, retraining, and adapting the model with new data, maintaining its relevance and performance in evolving environments.
Production Deployment
The selected model is deployed in the production environment. Continuous monitoring of the model’s functioning with the live data sets is required to ensure that it meets the defined objectives, use cases, and accuracy. To address this, the system must be equipped for constant monitoring and capable of evolving the model based on ongoing feedback. This adaptive approach is crucial for maintaining model effectiveness and accuracy in the dynamic automotive environment.
Watch out for:
- Inadequate Change Management
Implementing AI often requires changes in workflows and processes. If there’s insufficient change management in place to guide employees through these transitions, resistance and project failure can occur. There must be a gradual transition plan in place from existing to new methods of operation.
- Lack of Continuous Monitoring and Optimisation
Failure to establish mechanisms for continuous improvement can result in the degradation of results over time. Implementing the right MLOps system is essential to enhance AI system performance, ultimately helping to reduce the Total Cost of Ownership.
Author
Abhijit De
AI Technology Head, Tata Elxsi
Abhijit De brings over two decades of rich IT industry experience, with 18 of those years dedicated to Tata Elxsi. His expertise spans delivery management, client relations, operations, and R&D. With international experience, he drives revenue growth and operational efficiency, while ensuring quality and client satisfaction.