Development of AI-Based and Control-Based Systems for Safe and Efficient Operations of Connected and Autonomous Vehicles

Development of AI-Based and Control-Based Systems for Safe and Efficient Operations of Connected and Autonomous Vehicles

Samuel Labi Headshot - link directs to their research page
Samuel Labi
Sikai Chen Headshot - link directs to their research page
Sikai Chen
Purdue University Logo - link directs to Purdue research page

Principal Investigator(s):

Samuel Labi, Professor of Civil Engineering – Purdue University
Director – NEXTRANS
Associate Director – Center for Connected and Automated Transportation (CCAT)
Sikai Chen, Post-Doctoral Researcher – Center for Connected and Automated Transportation
Post-Doctoral Researcher – NEXTRANS Center at Purdue University
Visiting Research Fellow – Robotics Institute at the School of Computer Science at Carnegie Mellon University

Project Abstract:
This research is in three parts. The first part recognizes the range limitations of onboard sensors such as LiDAR and cameras, and develops an AI control system that fuses sensed (local) information and longer-range information to make CAV lane-changing decisions. Deep Reinforcement Learning is being used to provide an end-to-end framework that will help identify the optimal connectivity range for each domain of prevailing operating traffic density. The second part is developing a method to demonstrate a CAV’s catalytic efficacy for addressing stop-and-go traffic perturbations that adversely affects operational efficiency, fuel economy, emissions, travel time, and driver/passenger comfort. The third part is developing a collision avoidance framework for CAVs, to reduce the likelihood of collision with surrounding vehicles, particularly HDVs that drive aggressively or have uncertain or unpredictable behavior.

Research Thrust(s): Control & Operations, Enabling TechnologyModeling & Implementation

Project Form(s):
Project Information Form