Date: 8 May 2023
Time: 10:00 – 13:00 SGT
Location: SUTD Campus, 8 Somapah Rd, Singapore
The field of CAV stands at the confluence of three evolving disciplines – evolving sensors Internet of Things (IoT) technology, emerging standards for connectivity of vehicles, and growing applications of AI/Machine learning to wireless networking. The number of connected IoT devices are likely to grow from 9.5 billion devices in 2019 to 22.5 billion devices or more by 2025. More optimistic estimates project the number of IoT devices in 2025 to be 55 billion connected devices. Fueling the growth in the evolution of vehicles towards total automation is the development of novel sensors, 3D cameras, lidars and radars and their ability to connect to the Internet, and upload data to a cloud. The sensors of an autonomous vehicle collect anywhere from 1.4 TB to 19 TB of data per hour. Consequently, applications of IoT devices and sensors have rapidly expanded to integrate intelligent sensing and processing along with smart applications of the technology into various fields such as smart homes, smart appliances, enterprises, smart transportation including CAV, smart cities, agriculture, energy, security, healthcare, shopping, location-based services including tracking and other similar fields.
The vast amount of raw data collected must by mined for it to become useful in ensuring traffic safety by means such as intelligent rerouting of traffic or distribution of information on roadwork activities or accidents. Machine learning is a mechanism that has become extremely powerful in extracting meaningful data. A number machine learning algorithms exist and can be broadly classified under unsupervised, supervised, and reinforcement learning algorithms. A number of algorithms exist under each category.
With the advent of 5G and the next generation wireless technologies of 6G and beyond, artificial intelligence and machine learning will play a significant role at all levels of the protocol stack and in creating novel applications. This Workshop will address the impact of machine learning and their applications to CAV with several use cases.
Dr. Ebtesam Almazrouei – AI and 6G: Challenges and Opportunities for Wireless Networks and Autonomous Industries
The integration of Artificial Intelligence (AI) with 6G wireless networks has the potential to bring about a significant transformation in various industries, including wireless networks, autonomous driving, and other emerging technologies. This integration can further enhance the speed, reliability, and security of wireless networks, thereby enabling new possibilities for businesses and consumers. However, this integration also poses several challenges, such as the need for advanced wireless networking technologies and the development of new algorithms for processing the massive amounts of data generated by 6G networks. This session will focus on the challenges and opportunities of integrating AI with 6G in the context of wireless networks and autonomous industries.
Niels de Boer – Managing safety of AV functionality in an environment of AI interacting with human behavior
AV development is a difficult environment: a vehicle controlled by software is operating in an environment in which human (mis)behaviour plays a big factor. Traffic rules and drivers licence rules are written in a manner that is incompatible with good engineering principles. Perception systems need to accurately perceive the world around the vehicle, but will never be 100% correct. The presentation will go into the work CETRAN is doing on assessing safety of AVs in the light of these challenges.
Jinling Hu – CAV empowered by intelligence & connectivity
Future CAV will be developed by a systemS approach that is an integrated system involving Vehicles, Road, and Cloud. AI/ML will play an important role in realizing fully automated driving systems.
Some potential applications include:
• AI/ML in communication system
• AI/ML in CAV specific application scenarios
• Some examples
Intelligence and connectivity will be converged deeply, and AI/ML will be the technical enabler for CAV.
Dr. Malika Meghjani – Autonomous Mobility-on-Demand in Mixed Traffic Environments
Autonomous urban driving has a longstanding promise of deploying fully autonomous taxis and self-driving cars. However, the first step towards autonomy is integration of the self-driving cars in our existing infrastructure and being able to drive side-by-side human driven cars. In this talk, we highlight our initiatives of developing, simulating, and deploying Autonomous Mobility-on-Demand (AMoD) solutions in mixed traffic environments in Singapore. We discuss the fundamental building blocks of AMoD systems, the solutions and algorithms that we have developed and successfully deployed, and the challenges that we have encountered during the process.
Dr. Seshadri Mohan – AI/ML-Enabled Connected and Autonomous Vehicles in the Era of 5G, 6G, and Beyond, WWRF White Paper
With the advent of 5G and the next generation wireless technologies of 6G and beyond, artificial intelligence and machine learning will play a significant role at all levels of the protocol stack and in creating novel applications. This talk will be based on a White Paper under development and will address the impact of AI and machine learning and their applications to CAV with several use cases.