Autonomous Driving

Using computing to make driving safer.

DOMAINS | autonomous-driving | safety-critical-systems | vehicle-communication

PROJECTS | driver-behaviour-learning

METHODS | deep-learning | lstm

WEBPAGE: /news/2021-10-14-AutolineInterview/

Making self-driving cars is one of the great AI challenges of the 21st Century and it involves many different parts. The goal is not merely to make fully autonomous driving cars so that humans never need to drive cars again. In fact, there are many forms of automation to every aspect of driving and coordination of vehicles on the road that can be considered.

In my lab we have done work on a few focussed topics in this area:

  • Multi-Vehicle Communication - In a coordinated, multi-vehicle scenario such as a convoy or fleet or autonomous cars, it is important for the autonomous cars to communicate efficiently and reliably. In this topic we have looked at some ways to do this using Deep Neural Networks.
  • Driver Behaviour Learning - In this line of research we look at how humans drive and try to learn models of that which can be predictive with a good level of accuracy. If autonomous vehicles drive in ways similar to, although hopefully safer than, humans, then they can more easily be integrated into the existing roads and traffic.

Our Papers on Autonomous Driving

  1. IOTSMS
    Aggressive Driver Behavior Detection using Parallel Convolutional Neural Networks on Simulated and Real Driving Data
    Zehra Camlica, Jim Quesenberry, Daniel Carballo, and Mark Crowley
    In 9th International Confernece on Internet of Things: Systems, Management and Security (IOTSMS) IEEE, Milan, Italy, nov, 2022.
  2. patent
    Multi-Level Collaborative Control System With Dual Neural Network Planning For Autonomous Vehicle Control In A Noisy Environment
    Zhiyuan Du, Joseph Lull, Rajesh Malhan, Sriram Ganapathi Subramanian, Sushrut Bhalla, Jaspreet Sambee, Mark Crowley, Sebastian Fischmeister, Donghyun Shin, William Melek, Baris Fidan, Ami Woo, and Bismaya Sahoo.
    US Patent Office: #US 11,131,992 B2. sep, 2021.
  3. Deep Multi Agent Reinforcement Learning for Autonomous Driving
    Sushrut Bhalla, Sriram Ganapathi Subramanian, and Mark Crowley
    In Canadian Conference on Artificial Intelligence may, 2020.
  4. Learning Multi-Agent Communication with Reinforcement Learning
    Sushrut Bhalla, Sriram Ganapathi Subramanian, and Mark Crowley
    In Conference on Reinforcement Learning and Decision Making (RLDM-19) Montreal, Canada, 2019.
  5. Training Cooperative Agents for Multi-Agent Reinforcement Learning
    Sushrut Bhalla, Sriram Ganapathi Subramanian, and Mark Crowley
    In Proc. of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019) Montreal, Canada, 2019.
  6. Integration of Roadside Camera Images and Weather Data for monitoring Winter Road Surface Conditions
    Juan Carrillo, and Mark Crowley
    In Canadian Association of Road Safety Professionals (CARSP) Conference Calgary, Canada, 2019.
  7. Decision Assist for Self-Driving Cars
    Sriram Ganapathi Subramanian, Jaspreet Singh Sambee, Benyamin Ghojogh, and Mark Crowley
    In Canadian Conference on Artificial Intelligence Springer, Toronto, Ontario, Canada, 2018.