- Introduction
- SdV & SdM
- What is a software defined vehicle?
- What is a software defined mobility?
- Emerging Plans with Partners
SEA:ME (Software Engineering in Automotive & Mobility Ecosystems) is an open educational program that provides learners with the skills and knowledge needed to excel in the field of software-defined mobility (SdM) and software-defined vehicles (SdV). The program is designed for learners of all backgrounds, including high school students, apprenticeships students, university students, and industry professionals. It includes different learning modules (courses), simulation platforms, case studies, and open data sets, as well as project-based learning experiences and networking events. SEA:ME leverages open educational resources and platforms to create a more accessible and equitable learning experience, while also fostering collaboration and innovation in the field. By completing the program, learners will be equipped with the skills and knowledge needed to excel in the rapidly evolving field of SdM and SdV, and will be prepared for careers in this exciting and dynamic industry.
Software-defined vehicles (SdV) and software-defined mobility (SdM) are related concepts but they are not the same thing.
- SdV refers specifically to the integration of software and digital technologies into the design and operation of vehicles. This includes the use of sensors, cameras, connectivity, and other digital technologies to create smarter, more efficient, and more personalized vehicles. SdV also includes the development of autonomous vehicles that can operate without human input.
- On the other hand, SdM refers to the broader concept of using software and digital technologies to optimize all aspects of transportation and mobility, not just vehicles. SdM encompasses a wide range of technologies and approaches, including intelligent transportation systems (ITS), mobility-as-a-service (MaaS), connected vehicles, electric vehicles (EVs), and autonomous vehicles (AVs), as well as infrastructure, data analytics, and digital platforms that enable smarter and more efficient transportation systems.
In summary, while SdV is a subset of SdM, it focuses specifically on the integration of software and digital technologies into the design and operation of vehicles, while SdM is a broader concept that encompasses all aspects of transportation and mobility, including vehicles, infrastructure, and digital platforms.
Software-defined vehicle is a concept of designing and developing vehicles where the key functions of the vehicle are implemented and controlled through software. This includes functions such as engine management, brakes, steering, and other critical vehicle systems. The software-defined vehicle concept aims to create a more connected, intelligent, and customizable vehicle that can be updated and optimized remotely.
The software-defined vehicle concept is not a single project or technology, but rather a collection of different technologies and approaches that work together to create a software-defined vehicle. This includes things like vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, cloud computing, artificial intelligence, and more.
Automotive industry is talking about software-defined vehicles because it has the potential to revolutionize the way we design, build, and use vehicles. By integrating software into the core of vehicle design, manufacturers can create vehicles that are more flexible, efficient, and responsive to changing customer needs. The software-defined vehicle concept also enables manufacturers to remotely diagnose and fix issues, roll out updates and upgrades, and collect data to improve the overall driving experience.
Furthermore, with the advent of electric and autonomous vehicles, software-defined vehicle technologies are becoming increasingly important in ensuring these vehicles can operate safely and reliably. Overall, the software-defined vehicle concept is a key driver of innovation and progress in the automotive industry, and is likely to remain an important area of focus for years to come.
Introducing the concept of software-defined vehicles in educational projects can be a great way to help learners understand the importance of software in modern vehicles and the role it plays in shaping the future of transportation.
- Vehicle Control System: This includes developing software and hardware solutions for vehicle control systems such as propulsion, braking, steering, and suspension systems. The control systems play a crucial role in ensuring the safety and performance of the vehicle, especially in autonomous driving scenarios.
- Autonomous driving technology: This includes developing software and hardware solutions for autonomous vehicles, such as perception systems, decision-making algorithms, and control systems.
- Vehicle-to-Everything (V2X) communication: This involves developing systems and protocols that enable vehicles to communicate with other vehicles, infrastructure, and pedestrians to improve safety and efficiency.
- Human-machine interface (HMI): This involves developing user interfaces and interaction systems for vehicles that allow drivers and passengers to interact with the vehicle's software and hardware systems.
- Cybersecurity: This includes developing security solutions to protect SdV systems from cyber-attacks and ensuring the safety and privacy of passengers.
- Electrification: This involves developing software and hardware solutions for electric and hybrid vehicles, such as battery management systems and charging infrastructure.
- Data analytics: This involves analyzing data generated by SdV systems to improve vehicle performance, safety, and efficiency.
- Software-defined vehicle architecture: Developing and optimizing the software-defined architecture for vehicles, including integration with hardware components and defining interfaces and protocols.
- Building a simple vehicle control system
- Requirements: Students will build a simple vehicle control system using an Arduino microcontroller and various sensors such as accelerometers and gyroscopes. The system will be able to read sensor data and control a small model vehicle through different maneuvers. The project will also involve programming the control system to manage vehicle dynamics such as traction control and anti-lock braking.
- Timeline: 4-6 weeks
- Skills: Basic programming skills, knowledge of sensors and control systems, understanding of vehicle dynamics.
- Designing a modular vehicle control system
- Requirements: Students will design a modular vehicle control system that can be customized and expanded for different vehicle types and use cases. The system will include hardware modules for different functions such as engine control, braking, and steering, as well as a software framework for managing the modules and integrating with other vehicle systems. The project will involve designing and testing the hardware modules, as well as programming the software framework.
- Timeline: 12-16 weeks
- Skills: Basic electronics and programming skills, understanding of vehicle systems, ability to design and test modular hardware systems.
- Building a self-driving model car
- Requirements: Students will build a self-driving model car using a Raspberry Pi and various sensors such as cameras and LIDAR. The car will be programmed to navigate a simple obstacle course using machine learning algorithms such as deep neural networks. The project will also involve designing and testing control systems for vehicle dynamics and safety.
- Timeline: 8-12 weeks
- Skills: Basic programming skills, knowledge of machine learning algorithms and sensor integration, understanding of vehicle dynamics and safety.
- Developing a simulation platform for autonomous vehicles
- Requirements: Students will develop a simulation platform for testing and validating autonomous driving algorithms. The platform will include realistic models of vehicles, roads, and traffic scenarios, as well as tools for generating and analyzing data from simulated test runs. The project will involve designing and implementing the simulation platform, as well as testing and validating different autonomous driving algorithms.
- Timeline: 16-20 weeks
- Skills: Advanced programming skills, knowledge of simulation and modeling techniques, understanding of autonomous driving algorithms and sensor integration.
- Implementing V2X communication protocols
- Requirements: Students will implement V2X communication protocols such as Dedicated Short-Range Communication (DSRC) and Cellular-V2X (C-V2X) on a small model vehicle. The project will involve designing and testing the communication hardware and software, as well as developing applications for using V2X data to improve safety and efficiency.
- Timeline: 8-12 weeks
- Skills: Basic programming and electronics skills, knowledge of V2X communication protocols and applications.
- Designing a V2X network for smart cities
- Requirements: Students will design a V2X network for a smart city that can support communication between vehicles, infrastructure, and other devices. The project will involve analyzing traffic patterns and infrastructure needs, selecting appropriate communication technologies, and designing and testing the network architecture. The project will also involve developing applications for using V2X data to improve traffic flow and reduce congestion.
- Timeline: 16-20 weeks
- Skills: Advanced programming and network design skills, knowledge of V2X communication protocols and smart city infrastructure.
- Designing an intuitive HMI for a semi-autonomous vehicle
- Description: In this project, students will design an HMI for a semi-autonomous vehicle that is both intuitive and easy to use. They will need to consider factors such as user experience, safety, and accessibility, and design a system that can be easily adapted to different driving scenarios. Students will need to create wireframes, mockups, and prototypes of their HMI design, and test it in a simulated environment.
- Timeline: 6-8 weeks
- Skillset: User experience design, human factors engineering, software prototyping, simulation testing
- Developing a voice-controlled HMI for a smart vehicle
- Description: In this project, students will develop a voice-controlled HMI for a smart vehicle that can be used to perform various functions such as navigation, entertainment, and climate control. Students will need to design a natural language processing system that can understand spoken commands and respond appropriately, and integrate it with the vehicle's existing systems. They will also need to test the system in a simulated environment and fine-tune it based on user feedback.
- Timeline: 8-10 weeks
- Skillset: Natural language processing, machine learning, software development, testing and optimization
- Designing an augmented reality HMI for a connected vehicle
- Description: In this project, students will design an augmented reality HMI for a connected vehicle that provides drivers with real-time information about their surroundings. They will need to create a system that overlays digital information on top of the real-world environment, and can be used for navigation, safety, and entertainment purposes. Students will need to create a proof-of-concept prototype of their HMI design, and test it in a simulated environment.
- Timeline: 10-12 weeks
- Skillset: Augmented reality development, user experience design, software prototyping, simulation testing
- Implementing a secure communication protocol for a connected vehicle
- Description: In this project, students will design and implement a secure communication protocol for a connected vehicle that can protect against cyber threats such as hacking, malware, and data breaches. Students will need to research existing communication protocols and identify potential vulnerabilities, and design a protocol that can provide end-to-end encryption and authentication. They will also need to test the protocol in a simulated environment and evaluate its effectiveness.
- Timeline: 8-10 weeks
- Skillset: Network security, cryptography, software development, simulation testing
- Developing a threat detection and response system for a connected vehicle
- Description: In this project, students will develop a threat detection and response system for a connected vehicle that can detect and respond to cyber attacks in real-time. Students will need to create a system that can monitor the vehicle's systems and detect abnormal behavior, and respond appropriately by isolating the affected systems or initiating countermeasures. They will also need to test the system in a simulated environment and evaluate its effectiveness.
- Timeline: 10-12 weeks
- Skillset: Cybersecurity, machine learning, software development, simulation testing
- Conducting a vulnerability assessment for a software-defined vehicle
- Description: In this project, students will conduct a vulnerability assessment for a software-defined vehicle and identify potential security risks. They will need to analyze the vehicle's software architecture and identify potential attack vectors, and conduct penetration testing to simulate various cyber attacks. Students will then need to develop recommendations for improving the vehicle's security and present their findings to a group of stakeholders.
- Timeline: 12-14 weeks
- Skillset: Cybersecurity, penetration testing, risk assessment, presentation skills
- Design Simulation of Electric Vehicle Powertrain
- Requirements: Students will design and simulate the powertrain of an electric vehicle using a simulation software such as MATLAB/Simulink or OpenModelica. Students will need to understand the components and functionality of an electric vehicle powertrain, including the battery, motor, and controller. Students will need access to a computer with simulation software installed
- Timeline: 4-6 weeks
- Skillset: Students will learn about electric vehicle technology, powertrain design, simulation, and programming.
- Analysis of Vehicle Telemetry Data
- Requirements: Students will analyze telemetry data from a vehicle, such as speed, acceleration, and fuel consumption, and create visualizations to better understand the data. Students will need to collect and analyze the data using tools such as Python or R, and present their findings in a report or presentation
- Timeline: 3-4 weeks
- Skillset: Students will learn about data analytics, programming, and communication skills.
- Predictive Maintenance for Vehicles
- Requirements: Students will develop a predictive maintenance model for a vehicle using historical maintenance data. Students will need to collect and analyze data, develop and test their model, and present their findings in a report or presentation
- Timeline: 6-8 weeks
- Skillset: Students will learn about data analytics, machine learning, predictive modeling, and programming.
- Design and Implementation of Vehicle Data Logger
- Requirements: Students will design and implement a data logger to collect and store data from a vehicle, such as speed, acceleration, and fuel consumption. Students will need to understand the components and functionality of a data logger, as well as programming and electrical engineering skills. Students will need access to a vehicle and basic tools and equipment
- Timeline: 6-8 weeks
- Skillset: Students will learn about data logging, programming, electrical engineering, and project management.
- Design and develop a prototype of a software-defined vehicle architecture
- Requirements: The project involves designing and developing a prototype of a software-defined vehicle architecture that can manage various functions and applications of the vehicle. The architecture should be flexible, scalable, and customizable to accommodate different types of vehicles and functions. The project also involves testing and validating the architecture to ensure its reliability and efficiency.
- Potential Timeline: 6-12 months
- Skillset: Students will learn about the key principles of software-defined vehicle architecture, including the design and implementation of software, integration of hardware and software, and testing and validation of the architecture. They will also develop skills in programming, system design, and testing.
- Optimization of software-defined vehicle architecture for improved performance
- Requirements: The project involves optimizing the software-defined vehicle architecture to improve its performance and efficiency. This includes analyzing data from the vehicle and identifying areas of improvement, such as reducing latency and improving data processing speed. The project also involves developing and testing different optimization strategies and evaluating their impact on the performance of the architecture.
- Potential Timeline: 6-12 months
- Skillset: Students will learn about the principles of optimization in software-defined vehicle architecture, including data analysis, simulation, and testing. They will also develop skills in programming, data processing, and optimization techniques.
- Integration of software-defined vehicle architecture with cloud computing
- Requirements: The project involves integrating the software-defined vehicle architecture with cloud computing to enable remote monitoring, diagnostics, and updates of the vehicle. This includes developing and testing a cloud-based platform that can communicate with the vehicle's architecture and provide real-time data on its performance and status. The project also involves developing and testing different security measures to protect the data and the vehicle from cyber threats.
- Potential Timeline: 6-12 months
- Skillset: Students will learn about the integration of software-defined vehicle architecture with cloud computing, including data communication, security, and remote monitoring. They will also develop skills in programming, cloud computing, and cybersecurity.
Software-defined mobility refers to the concept of using software and digital technologies to improve and optimize all aspects of transportation and mobility. It involves using digital platforms, data analytics, and connectivity to create a more efficient, sustainable, and personalized mobility ecosystem.
The software-defined mobility concept encompasses a broad range of technologies and approaches, including:
- Intelligent Transportation Systems (ITS): Using sensors, cameras, and other technologies to collect data on traffic flow, congestion, and other factors to optimize transportation networks.
- Mobility-as-a-Service (MaaS): Providing users with a comprehensive and integrated digital platform for accessing various modes of transportation, including public transit, ride-sharing, bike-sharing, and more.
- Connected Vehicles: Integrating vehicles with digital platforms and networks to enable communication between vehicles, infrastructure, and other devices, improving safety, and efficiency.
- Electric Vehicles (EVs): Using digital platforms and technologies to optimize charging, monitor battery health, and manage energy consumption to maximize the efficiency and performance of EVs.
- Autonomous Vehicles (AVs): Using software and machine learning algorithms to enable vehicles to operate without human input, improving safety, and efficiency.
Overall, software-defined mobility is about creating a more connected, efficient, and sustainable transportation ecosystem that is capable of adapting to the needs and preferences of individuals and communities.
There are many project areas in software-defined mobility (SdM) that can be pursued by researchers, students, and industry professionals. Some of the key project areas include:
- Intelligent Transportation Systems (ITS): Developing and implementing advanced ITS solutions to improve the safety, efficiency, and sustainability of transportation systems. This can include technologies such as intelligent traffic management systems, advanced traveler information systems, and vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication systems.
- Mobility-as-a-Service (MaaS): Developing and implementing MaaS solutions that provide users with integrated and personalized mobility options, such as ride-hailing, public transit, car-sharing, and micro-mobility options. This can include the development of digital platforms, data analytics tools, and integrated payment systems.
- Connected Vehicles: Developing and testing advanced technologies that enable vehicles to communicate with each other and with infrastructure, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication systems. This can include the development of communication protocols, security and privacy measures, and data analytics tools.
- Autonomous Vehicles: Developing and testing advanced autonomous driving systems that enable vehicles to operate without human intervention. This can include the development of sensor and communication systems, machine learning algorithms, and safety and security protocols.
- Electric Vehicles (EVs): Developing and implementing EV solutions that promote the adoption of sustainable and low-carbon transportation. This can include the development of advanced battery technologies, charging infrastructure, and energy management systems.
- Smart Cities: Developing and implementing SdM solutions at the city or regional level, such as smart traffic management systems, smart parking solutions, and integrated transportation and land-use planning. This can include the development of data analytics tools and decision support systems to optimize urban transportation systems.
Overall, the project areas in SdM are diverse and interdisciplinary, and offer many opportunities for research, innovation, and collaboration. By pursuing projects in these areas, researchers, students, and industry professionals can contribute to the development of more efficient, sustainable, and equitable transportation systems.
- A real-time traffic monitoring and prediction system
- Requirements: Students will develop a system that collects and analyzes traffic data in real-time, and uses machine learning algorithms to predict traffic conditions and congestion. The system should also provide recommendations for alternative routes and modes of transportation to users.
- Potential Timeline: 3-4 months
- Skillset: Data collection and analysis, machine learning algorithms, programming, database management, user interface design
- An advanced traveler information system
- Requirements: Students will design a digital platform that provides users with personalized and real-time information about transportation options, traffic conditions, and weather conditions. The system should be user-friendly and accessible through multiple devices and platforms.
- Potential Timeline: 2-3 months
- Skillset: User interface design, data visualization, programming, database management, user research
- A ride-hailing platform for a specific city
- Requirements: Students will develop a digital platform that connects users with ride-hailing services in a specific city, and provides real-time information about prices, availability, and estimated arrival times. The platform should also allow users to pay for rides and rate drivers.
- Potential Timeline: 3-4 months
- Skillset: User interface design, programming, database management, payment systems, user research
- Analyzing user behavior in a car-sharing program
- Requirements: Students will analyze data from a car-sharing program, and identify patterns and trends in user behavior, such as usage patterns, trip duration, and distance traveled. Students will then use this analysis to develop recommendations for improving the car-sharing program.
- Potential Timeline: 2-3 months
- Skillset: Data analysis, statistical modeling, programming, database management, user research
- A V2V communication protocol
- Requirements: Students will develop a communication protocol that allows vehicles to communicate with each other in real-time, and exchange information about speed, direction, and location. The protocol should also incorporate security and privacy measures to protect user data.
- Potential Timeline: 4-5 months
- Skillset: Networking protocols, programming, security and privacy, user research
- A connected vehicle dashboard
- Requirements: Students will design a user-friendly dashboard that displays real-time information about vehicle performance, traffic conditions, and weather conditions. The dashboard should also allow users to control vehicle settings and access entertainment options.
- Potential Timeline: 2-3 months
- Skillset: User interface design, data visualization, programming, database management, user research
- An autonomous vehicle simulation platform
- Requirements: Students will develop a simulation platform that allows users to test and optimize autonomous driving systems in a virtual environment. The platform should incorporate realistic traffic and weather conditions, and allow users to customize the simulation settings.
- Potential Timeline: 4-5 months
- Skillset: Simulation software development, programming, machine learning algorithms, user research
- Testing an autonomous shuttle service in a controlled environment
- Requirements: Students will test an autonomous shuttle service in a controlled environment, such as a closed campus or parking lot. Students will analyze the performance of the autonomous shuttle, and identify areas for improvement.
- Potential Timeline: 3-4 months
- Skillset: Testing and evaluation, data analysis, statistical modeling, programming, user research
- An EV charging station network
- Requirements: Students will develop a plan for a network of EV charging stations, taking into account factors such as location, capacity, and cost. Students will also develop a user-friendly interface for the charging station network, and analyze the impact of the network on the local community.
- Potential Timeline: 4-5 months
- Skillset: Urban planning, user interface design, programming, database management, user research
- An EV conversion kit
- Requirements: Students will design and build an EV conversion kit that can be used to convert a gasoline-powered vehicle to an electric vehicle. The kit should include components such as a motor, battery pack, and controller, and should be compatible with a wide range of vehicles.
- Potential Timeline: 6-7 months
- Skillset: Mechanical engineering, electrical engineering, programming, user research
- A smart traffic management system
- Requirements: Students will design and develop a traffic management system that uses real-time traffic data and machine learning algorithms to optimize traffic flow in a specific city. The system should be scalable and adaptable to changing traffic conditions.
- Potential Timeline: 5-6 months
- Skillset: Machine learning algorithms, programming, data analysis, user research, user interface design
- A smart waste management system
- Requirements: Students will develop a waste management system that uses sensors and machine learning algorithms to optimize waste collection and disposal in a specific city. The system should be designed to reduce waste and improve recycling rates.
- Potential Timeline: 4-5 months
- Skillset: Machine learning algorithms, programming, sensor technology, data analysis, user research
- A smart energy management system
- Requirements: Students will design and develop an energy management system that uses real-time energy data and machine learning algorithms to optimize energy use in buildings and public spaces in a specific city. The system should be designed to reduce energy consumption and improve energy efficiency.
- Potential Timeline: 6-7 months
- Skillset: Machine learning algorithms, programming, sensor technology, data analysis, user research
SEA:ME partners with Microsoft to develop four proof-of-concept projects focused on V2C, digital trip book, airport experience, and MaaS topics. The projects will use Azure cloud solutions for data storage and analysis, while SEA:ME provides expertise in SdV, software development, and UX design. The partnership aims to provide students with hands-on, project-based learning opportunities in the automotive industry. Additionally, SEA:ME is developing educational projects utilizing Azure IoT solutions for connected vehicles and personalized driving recommendations. The partnership offers a unique opportunity for students to gain practical experience in automotive and mobility software engineering and cloud computing.
SEA:ME can integrate CARIAD's latest work in the automotive and mobility sectors, including VW.OS, VW.AC, The Big Loop, intelligent cockpit, automated driving, and data-driven development. This collaboration can lead to exciting educational projects that benefit both SEA:ME students and CARIAD's research and development initiatives. Projects may include developing applications on VW.OS, services on VW.AC, analyzing data through The Big Loop, designing intelligent cockpits, developing automated driving systems, and cybersecurity. Key contacts for strategic collaboration include Thomas Fleischmann and Dr. Florian Meyer.
The collaboration with MBition aims to bridge the gap between industry and academia by introducing a range of exciting projects and initiatives to the SEA:ME curriculum. The partnership will focus on developing software-defined vehicles, building new user experience features, re-defining software integration processes, exploring the potential of the MBOS platform, and cybersecurity and safety. The collaboration will provide hands-on, project-based learning opportunities for students, enabling them to gain practical experience with cutting-edge technologies and tools used in the automotive industry. The collaboration will require at least four developers, with one developer needed for each of the four potential work areas. Fatih Tekin and Christoph Möhren are the key contacts for this strategic collaboration.
SEA:ME and T-Systems have partnered to enhance the automotive and mobility software engineering education curriculum by providing students with hands-on, project-based learning opportunities. The collaboration aims to develop educational projects on tele-operated driving, see-through technology, software-defined cars, digital twin tech, and cloud-based services. The partnership offers students the chance to work with advanced tools and technologies relevant to the industry. The collaboration involves key contacts from both organizations and requires at least one developer for each project.
Bosch is a global technology company that is heavily involved in the automotive software engineering field, particularly in the development of advanced driver assistance systems (ADAS) and autonomous driving systems. The two entities could collaborate on potential work areas such as ADAS development, vehicle communication, cloud-based services, cybersecurity, and autonomous vehicles. Potential educational projects that could be developed include developing a lane departure warning system, automated emergency braking system, secure over-the-air (OTA) software updating system, and autonomous vehicle control systems. Bosch's Automotive Electronics division, Connected Mobility Solutions division, and Bosch Center for Artificial Intelligence (BCAI) are potential strategic collaboration partners, while key contacts include Alexander Eckert, Jan Bechtold, Ansgar Lindwedel, Dirk Slama, Philipp Mundhenk, Anke Giliard, and Michael Herman.
Capgemini and SEA:ME could collaborate on developing educational projects for students in the automotive and mobility sector. Capgemini's expertise in personalization features, voice assistant systems, assisted driving technologies, software-defined vehicles, and mobility as a service (MaaS) could be shared to develop hands-on projects. The collaboration could lead to educational projects development such as personalization in MaaS using K8s and cloud solutions, a voice assistant system for cars, and software-defined steering, braking, and acceleration systems. Key contacts for the collaboration are Nino Nikolaishvili and Amir Sanei.
SEA:ME and Digiteq Automotive have partnered to provide innovative and practical projects for automotive and mobility software engineering, such as developing park, adaptive cruise, and reverse assistance systems, and using advanced technologies like computer vision and machine learning to develop safety-critical software components. The partnership aims to drive exciting projects that benefit the industry and SEA:ME participants. The potential work areas include park assistance, adaptive cruise control, reverse assistance, HiL testing systems, test automations using robotic arms, perception and maneuvering, Autosar design, microstack design, stereovision perception, ADAS with trailer, driver emotion detection systems, and low hazard detection systems. The program aims to provide students with the skills and knowledge to design and implement complex systems using sensors, cameras, and control algorithms, integrate multiple sensors and control modules, use computer vision algorithms to detect and classify objects in real-time, and validate and optimize control algorithms using simulation tools and techniques.
The collaboration with MicroNova can provide students with hands-on experience in advanced automotive testing and simulation technologies. Potential educational projects include HiL simulation for ADAS, EXAM test automation for autonomous driving systems, virtualization SiL for connected vehicle testing, and integration of HiL and SiL technologies. Through these projects, students can gain exposure to industry-leading technologies and develop skills highly relevant to the automotive sector. Each project has specific requirements and benefits for students, including learning about the latest trends and developments in automotive technology, developing skills in building and testing various components, and gaining hands-on experience in testing different automotive systems.