My Research

My doctoral background lies broadly in intelligent transportation systems, where I approached problems from the perspectives of multi-agent and cyber–physical systems. My primary research objective is to study the mathematical modeling, connectivity, and control design aspects of connected vehicles (CVs) to enable safe cooperative movement. This includes ensuring safe inter-vehicular spacing and improved traffic flow efficiency on both lane-based and lane-less urban roads under heterogeneous traffic conditions, where vehicles exhibit varying engine/powertrain characteristics and model parameters, along with the coexistence of human-driven vehicles. I designed decentralized control algorithms that can be implemented for automation in CVs based on the non-linear sliding mode control and optimal control principles. For validation, I performed extensive simulations on the real-time traffic simulator platform, Simulation of Urban Mobility (SUMO), and benchmarked against real-world vehicle trajectory datasets wherever available.

Some of the graphic output of the simulations conducted on Simulation of Urban MObility (SUMO) traffic simulator platform are shown below

SUMO (lanechange-D).mp4

The above video describes the lane-changing maneuver performed by an autonomous/semi-autonomous connected vehicle (CV) in a heterogeneous traffic environment under unreliable communication, i.e., packet drops and/or communication delays in the vehicle-to-vehicle (V2V) communication channel.

SUMO (lane-less-D).mp4

The above video illustrates CVs with random initial positions (on lane-less roads), which progressively self-organize in the form of layers, referred to as the layered formation while ensuring collision avoidance.

SUMO(Lane-less Mixed-D).mp4

This video shows the movement of CVs in mixed traffic with human-driven vehicles (HDVs) on lane-less roads, where CVs ensure a safe dynamic layered formation despite any random driving behavior of HDVs.

The control algorithms for each CV are computed in MATLAB/SIMULINK using the traffic state information extracted from SUMO via an internal tool called the Traffic Control Interface (TraCI). These control signals are then sent back to the ongoing SUMO simulation via TraCI. (for real-time validation of proposed design)

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