Connected and Automated ICE Vehicles

Detailed overview of innovation with sample startups and prominent university research

What it is

Connected and automated vehicles (CAVs) encompass a spectrum of technologies that enhance vehicle connectivity and automation:

  • Connected Vehicles: These vehicles utilize communication technologies, such as Vehicle-to-Everything (V2X) communication, to exchange data with other vehicles, infrastructure, and cloud-based services, enabling real-time information sharing and cooperative driving.
  • Automated Vehicles: These vehicles, often referred to as self-driving or autonomous vehicles, utilize advanced sensors, artificial intelligence (AI), and control systems to automate driving tasks, ranging from lane keeping and adaptive cruise control to fully autonomous navigation.

Impact on climate action

Connected and Automated Vehicles revolutionize low-carbon ICE vehicles by optimizing routes, reducing congestion, and enhancing fuel efficiency through real-time data exchange. This innovation streamlines transportation, curbing emissions and promoting sustainable urban planning. Its integration fosters a significant shift towards eco-conscious mobility, amplifying the momentum of climate action initiatives.


  • Vehicle-to-Everything (V2X) Communication: V2X allows vehicles to communicate with each other (V2V), with infrastructure (V2I), and with pedestrians (V2P), enabling real-time data exchange about traffic conditions, road hazards, and other relevant information.
  • Advanced Sensors: CAVs utilize an array of sensors, including radar, lidar, cameras, and GPS, to perceive their surroundings and gather data about the environment.
  • Artificial Intelligence (AI) and Machine Learning: AI algorithms process sensor data and make real-time decisions on vehicle control, navigation, and safety, while machine learning models improve system performance over time by learning from data.
  • Cloud Computing and Big Data: Cloud platforms provide the computational power and storage capacity to process vast amounts of data generated by CAVs, enabling real-time analysis, traffic prediction, and optimization of routing algorithms.

TRL : Variable (6-9)

Prominent Innovation themes

  • Eco-Cooperative Adaptive Cruise Control (CACC): This technology uses V2V communication to enable vehicles to follow each other at closer distances, reducing aerodynamic drag and improving fuel efficiency, particularly in highway driving.
  • Predictive Eco-Routing: Leveraging real-time traffic data, navigation systems, and AI algorithms to predict traffic flow and suggest the most fuel-efficient routes, minimizing congestion and reducing fuel consumption.
  • Automated Platooning: Using V2V communication to allow trucks to travel in close formation, significantly reducing aerodynamic drag and improving fuel efficiency for long-haul trucking.
  • Intersection Optimization: Employing V2I communication to optimize traffic flow at intersections, minimizing idling times and reducing fuel consumption and emissions.
  • Eco-Driving Assistance Systems: Developing AI-powered systems that provide real-time feedback and coaching to drivers, encouraging smoother acceleration and braking, optimal speed management, and other eco-driving techniques.

Other Innovation Subthemes

  • Cooperative Vehicle Communication
  • Advanced Sensor Fusion
  • Intelligent Decision-Making Algorithms
  • Cloud-Based Data Analytics
  • Vehicle-to-Everything (V2X) Integration
  • AI-Driven Traffic Optimization
  • Predictive Eco-Routing Solutions
  • Automated Platooning Technologies
  • Intersection Traffic Management
  • Eco-Driving Assistance Systems
  • Seamless Vehicle Connectivity
  • Sensor-Based Environmental Awareness
  • Real-Time Traffic Prediction
  • Fuel-Efficient Navigation Systems
  • Automated Traffic Flow Control

Sample Global Startups and Companies

  • Peloton Technology:
    • Technology Focus: Peloton Technology specializes in connected vehicle systems for trucking fleets. They develop technologies that enable platooning, where trucks autonomously follow each other closely to reduce aerodynamic drag and improve fuel efficiency.
    • Uniqueness: Peloton’s uniqueness lies in its focus on platooning technology specifically tailored for the trucking industry. By leveraging vehicle-to-vehicle communication and automated driving features, they optimize fleet operations and safety.
    • End-User Segments: Their primary customers are trucking companies looking to improve fuel efficiency, reduce operating costs, and enhance safety. Industries relying heavily on freight transportation, such as logistics and retail, could benefit from Peloton’s solutions.
  • Waymo:
    • Technology Focus: Waymo is a leading company in autonomous vehicle technology, focusing on developing self-driving cars for various applications, including ride-hailing and delivery services.
    • Uniqueness: Waymo stands out for its extensive testing and real-world deployment of autonomous vehicles. They have accumulated vast amounts of data and experience, which contributes to the reliability and safety of their self-driving technology.
    • End-User Segments: Waymo’s technology has broad applications, including transportation for individuals, goods delivery, and last-mile logistics. Their target segments include transportation network companies, delivery services, and municipalities interested in autonomous mobility solutions.
  • Aurora:
    • Technology Focus: Aurora focuses on developing self-driving technology for a range of vehicle platforms, including passenger cars, trucks, and shuttles. They emphasize a combination of software, hardware, and data to create safe and efficient autonomous systems.
    • Uniqueness: Aurora’s uniqueness lies in its approach to building a universal autonomous driving platform adaptable to various vehicle types and use cases. They prioritize safety and scalability in their technology development.
    • End-User Segments: Aurora’s technology caters to a wide range of industries, including automotive manufacturers, ride-hailing companies, and logistics providers. Their flexible platform allows for customization to meet the specific needs of different end-users.

Sample Research At Top-Tier Universities

  • Massachusetts Institute of Technology (MIT):
    • Technology Enhancements: MIT researchers are focusing on integrating connected and automated vehicle (CAV) technologies with low-carbon ICE vehicles to optimize their fuel efficiency and reduce emissions. They are developing advanced algorithms for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication to enable coordinated driving and eco-driving strategies.
    • Uniqueness of Research: MIT’s approach involves a holistic optimization of the transportation system by considering the interactions between vehicles, infrastructure, and traffic flow. They are leveraging big data analytics and machine learning techniques to predict traffic patterns and optimize routing and scheduling of low-carbon ICE vehicles in real-time.
    • End-use Applications: The research at MIT has implications for various stakeholders, including automotive manufacturers, policymakers, and urban planners. By integrating CAV technologies with low-carbon ICE vehicles, cities can reduce traffic congestion, improve air quality, and enhance overall mobility while minimizing carbon emissions.
  • Stanford University:
    • Technology Enhancements: Stanford researchers are exploring novel approaches to enhance the performance and efficiency of low-carbon ICE vehicles through connected and automated driving systems. They are developing advanced sensing and perception technologies to enable vehicles to communicate with each other and with the surrounding environment for safer and more efficient driving.
    • Uniqueness of Research: Stanford’s research focuses on the integration of artificial intelligence (AI) and machine learning algorithms with connected vehicle systems to enable autonomous decision-making in complex traffic scenarios. They are investigating human-centered design principles to ensure user acceptance and trust in automated driving technologies.
    • End-use Applications: The research at Stanford has applications in both passenger and commercial vehicle sectors, including ride-sharing, logistics, and public transportation. By deploying connected and automated driving systems in low-carbon ICE vehicles, companies can improve fleet management, reduce operating costs, and enhance overall safety and reliability.
  • Carnegie Mellon University:
    • Technology Enhancements: Carnegie Mellon researchers are developing innovative solutions to enhance the energy efficiency and sustainability of low-carbon ICE vehicles through connected and automated driving technologies. They are exploring vehicle-to-everything (V2X) communication protocols and cooperative driving strategies to optimize fuel consumption and reduce greenhouse gas emissions.
    • Uniqueness of Research: Carnegie Mellon’s research emphasizes the integration of human factors and social acceptance considerations into the design and implementation of connected and automated vehicle systems. They are conducting interdisciplinary research that combines engineering, psychology, and policy analysis to address the societal challenges and opportunities associated with autonomous driving.
    • End-use Applications: The research at Carnegie Mellon has implications for various stakeholders, including automakers, transportation agencies, and environmental organizations. By deploying connected and automated driving technologies in low-carbon ICE vehicles, cities can improve traffic flow, enhance road safety, and promote sustainable urban mobility while reducing their carbon footprint.

commercial_img Commercial Implementation

Connected vehicle technologies, such as V2X communication and advanced driver assistance systems (ADAS), are already commercially available and being implemented in many production vehicles. Many automotive manufacturers offer features like adaptive cruise control, lane keeping assist, and automatic emergency braking, which leverage connectivity and sensor data to enhance safety and efficiency. Fully autonomous vehicles are still in limited commercial deployment, primarily in controlled environments and pilot programs.