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Lokesh Chandra Das

4 accepted papers

2025

MATRICS: A Multi-Agent Deep Reinforcement Learning-Based Traffic-Aware Intelligent Lane-Change System

IROS 2025

We present MATRICS, a traffic-aware multi-agent reinforcement learning (MARL)-based intelligent lane-change system designed for autonomous vehicles (AVs). While existing research primarily focuses on enhancing the local impact of the ego vehicle’s lane-change decisions, MATRICS stands out by optimiz

Cited by 0SourceScholar
2023

RLPG: Reinforcement Learning Approach for Dynamic Intra-Platoon Gap Adaptation for Highway On-Ramp Merging

IROS 2023poster

A platoon refers to a group of vehicles traveling together in very close proximity using automated driving technology. Owing to its immense capacity to improve fuel efficiency, driving safety, and driver comfort, platooning technology has garnered substantial attention from the autonomous vehicle re…

Cited by 3SourceScholar
2023

WatchPed: Pedestrian Crossing Intention Prediction Using Embedded Sensors of Smartwatch

IROS 2023poster

The pedestrian crossing intention prediction problem is to estimate whether or not the target pedestrian will cross the street. State-of-the-art techniques heavily depend on visual data acquired through the front camera of the ego-vehicle to make a prediction of the pedestrian's crossing intention.…

Cited by 1SourceScholar
2021

SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning

ICML 2021spotlight

We present a novel adaptive cruise control (ACC) system namely SAINT-ACC: {S}afety-{A}ware {Int}elligent {ACC} system (SAINT-ACC) that is designed to achieve simultaneous optimization of traffic efficiency, driving safety, and driving comfort through dynamic adaptation of the inter-vehicle gap based…

Cited by 25SourcePDFScholar