Panel Discussion: Role of Semiconductors in Shaping the Software-Defined Vehicle Era

11:45–12:45 p.m.

The automotive industry is experiencing a transformation with the emergence of software-defined vehicles (SDVs), where software capabilities increasingly define vehicle functionality and user experience. Semiconductors enable the seamless integration of computing, communication, and control systems in modern vehicles. This panel discussion will bring together industry leaders from the semiconductor sector, original equipment manufacturers (OEMs), and Tier-1 integrators to explore the technical challenges and opportunities in advancing a more software-centric automotive future. The conversation will focus on how technologies can drive innovation in SDVs, support computation power, adherence to safety and security, enhance connectivity and ultimately, and redefine customer experience in the automotive landscape.

SDV: Migration of Non-AUTOSAR Applications to Adaptive AUTOSAR Applications

14:15–14:45

This talk presents a detailed exploration of migrating classic AUTOSAR or non-AUTOSAR components to AUTOSAR Adaptive using Model-Based Design. The session covers the advantages of AUTOSAR Adaptive, the differences between classic and Adaptive AUTOSAR, and the technical methods and challenges involved in this migration.

This migration aims to leverage the flexibility and benefits of AUTOSAR Adaptive to transition functionalities from classic or non-AUTOSAR environments to AUTOSAR Adaptive. KPIT has undertaken this project to assist the customer in migrating existing functionalities while maintaining behavior and meeting all necessary Automotive SPICE development life-cycle requirements, achieving SWE 1 to SWE 6 maturity levels.

MATLAB^® and Simulink^® products have been integral to this process, supporting model-based development, code generation, and model-in-the-loop (MIL) testing. Chosen for their user-friendliness, efficiency, and ease of integrating Adaptive AUTOSAR wrapper layer blocks, these products have been crucial in executing the migration smoothly while preserving control logic.

ADAS: Virtual Validation of Warning-Based ADAS Features

14:45–15:15

Advanced driver-assistance systems (ADAS) are becoming crucial for enhancing the safety of road users and vehicles. Developing ADAS features requires the development of sophisticated algorithms, involving sensors and control systems to perceive the environment, make decisions, and execute driving maneuvers. Validating ADAS features, such as lane departure warning (LDW) and forward collision warning (FCW), is essential for improving vehicle safety, especially on Indian roads. To ensure these features are robust, extensive validation is required. Virtual validation offers a scalable and efficient solution for this purpose. In this session, we talk about some of the key components of algorithm and validation:

• Automating data annotation for vision perception algorithm development
• Accurately creating realistic scenes and scenarios for virtual validation including custom 3D assets
• Modeling virtual sensors (cameras) and vehicles (trucks) to reflect real-world conditions
• Integrating ADAS algorithms in a virtual environment to evaluate performance and reliability in various scenarios
• Creating custom metrics to assess the performance of FCW and LDW features

Tech Talk: Driving the Future: Integrating ADAS in SDVs with Model-Based Design

15:45–16:45

The automotive industry is undergoing significant transformations with the advent of software-defined vehicles (SDVs) and advanced driver-assistance systems (ADAS). These megatrends are marked by the integration of advanced software and centralized computing platforms, fundamentally reshaping vehicle architecture and operational efficiency. A key aspect of this evolution is the consolidation of electronic control units (ECUs), which streamlines vehicle systems and enhances performance. SDVs employ high-performance computing to manage various functions, including ADAS, which improves driver safety and convenience with features like adaptive cruise control, lane-keeping assistance, and automated parking. 

The transition from traditional desktop-based development processes to continuous integration/continuous deployment (CI/CD) and DevOps workflows signifies a pivotal shift toward more agile and efficient development practices. Over-the-air (OTA) software updates enable continuous improvements and adaptation to emerging technologies, ensuring that SDVs and ADAS remain at the forefront of innovation. This evolution is redefining the driving experience, offering increased efficiency, flexibility, and enhanced safety. 

In this panel discussion, we explore the following key areas: 

• Technological foundations for ADAS and SDVs, including sensors, algorithms, centralized architecture, and validation processes
• Development challenges and insights into complexities of integrating ADAS and SDVs into vehicle platforms
• DevOps and OTA updates for rapid deployment of new features, ensuring compliance with safety and security standards
• Legacy software migration transitioning to service-oriented architecture (SOA) to achieve modular and scalable designs
• MathWorks role in accelerating the development of ADAS and vehicle software architecture through Model-Based Design 

Analyzing eDrive Performance in Electric Vehicles Using MATLAB and Simulink

14:15–14:45

Electric vehicle (EV) systems consist of components that interconnect in a coordinated manner. The HV battery, traction inverter, electric motor, and transmission system are the major components in an EV. Among these, the traction inverter and the electric machine together are known as the eDrive. The performance of the eDrive in EVs is crucial for several reasons. First, it directly impacts the acceleration and speed capabilities of the vehicle. A powerful and efficient eDrive system ensures that EVs can achieve adequate acceleration and maintain higher speeds, providing a more enjoyable driving experience for users. Second, the performance of the eDrive influences the overall range and efficiency of the EV. A well-designed eDrive system can maximize the energy utilization, enabling the vehicle to travel longer distances on a single charge.

This session presents a solution using MATLAB^® and Simulink^® to analyze the performance of the eDrive. The thermal (power module temperature and electric machine temperature), electrical (voltage and current), and mechanical (torque and speed) parameters are chosen to analyze the eDrive performance.

Accelerating EV Thermal Controller Development Using RCP on Speedgoat

14:45–15:15

Electric vehicle (EV) thermal systems must operate with high efficiency to enhance range and performance. Effective control of various thermal components is essential to minimize energy waste, but during the vehicle development process, not all electronic control units (ECUs) are available, and function development is in its early stages. This makes calibration, testing, and efficiency evaluation challenging. 

To overcome these issues, Mahindra & Mahindra developed a solution that enables systems engineers to adjust vehicle controls without the constraints of hardware controllers. Using Simulink^®, Simscape^®, and the Speedgoat platform, we created a flexible environment for functional logic development and seamless vehicle system behavior verification.  This presentation explores the challenges of controlling thermal components when ECUs are unavailable during development. We present a solution that enables real-time vehicle control adjustments without hardware configuration. Attendees will learn how Simulink, Simscape, and Speedgoat streamline the development process, allowing better control and testing of vehicle systems and leading to more efficient and effective vehicle designs. 

Our approach enhances controller development, integrates prototyping tools, optimizes control strategies, and supports continuous integration within the development cycle. By leveraging MathWorks products and services, OEMs can improve EV thermal management system design, testing, and optimization. This comprehensive toolset supports all stages of development, from modeling and simulation to real-time testing, ultimately contributing to faster time-to-market, improved system performance, and reduced development costs—aligning with the strategic goals of advanced EV development. 

Tech Talk: Virtual Vehicle: Transforming Vehicle Engineering Through Simulation

15:45–16:45 p.m.

This panel discussion explores how the automotive industry is leveraging simulation and Model-Based Design to revolutionize vehicle engineering. From system modeling and controls engineering to validation and large-scale studies, industry experts discuss the transformative impact of creating virtual vehicles. Real-world examples, including AMZ Racing’s record-breaking motor controller design and Rivian’s scalable simulation platform, highlight how virtual development accelerates innovation, reduces costs, and ensures robust vehicle performance. Attendees will gain insights into cutting-edge simulation techniques driving the future of vehicle engineering.

TATA ServiceSage: A Gen AI–Based RCA Chat Assistant

14:15–14:45

The AI-powered ServiceSage chatbot for Tata Motors vehicles harnesses advanced natural language processing (NLP) and generative AI technologies to transform vehicle diagnostics and maintenance. Built on a retrieval-augmented generation (RAG) architecture, this intelligent system integrates large language models (LLMs) to efficiently extract and interpret complex information from service manuals. This approach enables ServiceSage to offer dynamic diagnostic capabilities, enabling precise, real-time query resolution.

ServiceSage retains chat history for contextual continuity and leverages continuous learning algorithms to evolve based on user feedback, ensuring that the system adapts and improves over time. Designed to enhance operational efficiency, the system significantly improves diagnostic accuracy, reduces repair times, and elevates the overall service experience for both technicians and vehicle owners. By offering up-to-date, context-aware, and accurate support, ServiceSage sets a new standard for automotive service operations.

Embedded AI for Body Applications with MATLAB and Simulink

14:45–15:15

At the outset of our project aimed at advancing smarter next-generation body interiors, we encountered several challenges: acquiring a technological edge without disrupting existing development processes, addressing tool dependencies, and deploying solutions on resource-constrained devices.

In this session, we describe how we developed an efficient embedded AI workflow, encompassing everything from machine learning concept design to code generation and deployment on the target ECU, all while integrating seamlessly with our existing processes.

This project was executed in collaboration with MathWorks India, leveraging Statistics and Machine Learning Toolbox™, Deep Learning Toolbox™, and automatic code generation to accelerate the deployment of vehicle software. The use of neural network optimization, quantization, and projection techniques has the potential to significantly minimize memory usage and computational demands, enabling deployment on resource-constrained devices.

This project establishes a basis for future embedded AI development in body and comfort systems, and we are in the process of integrating this workflow into our current development process.

Tech Talk: Accelerating AI Adoption: From Design to Deployment in Mobility

15:45–16:45

AI is revolutionizing the mobility sector by enhancing vehicle capabilities, optimizing traffic systems, and supporting sustainable transportation solutions. The integration of AI technologies into mobility platforms enables advancements such as autonomous driving, real-time traffic management, and smart city infrastructure. AI-driven analytics and predictive modeling offer significant improvements including accelerated vehicle model development and sensor reductions. As AI continues to evolve, it reshapes the landscape of mobility, providing increased safety, efficiency, and adaptability.

In this session, we begin with an overview of AI’s impact on mobility, followed by an in-depth exploration of the following topics:

• Technological foundations: Examine the core technologies enabling AI in mobility, including machine learning algorithms, sensor integration, and data-driven architectures.
• Deployment challenges: Discuss the hurdles in deploying AI solutions, such as data privacy, ethical considerations, and regulatory compliance.
• Deployment strategies: Explore strategies for scalable and efficient AI deployment, focusing on infrastructure, iterative testing, and cross-industry collaboration.
• Legacy system integration: Address the transition from traditional systems to AI-enhanced architectures, highlighting modular and scalable design principles.
• Industry and academia collaborations: Learn how industry-academia collaboration is reshaping the adoptability of AI in automotive applications.

This session provides insights into AI’s transformative potential in mobility, emphasizing design, deployment, and future innovations.