Integrating Reinforcement Learning and Contextual Analysis for Enhanced Pedestrian Behavior Prediction

International Journal of Electronics and Communication Engineering

© 2026 by SSRG - IJECE Journal

Volume 13 Issue 1

Year of Publication : 2026

Authors : Giribabu Sadineni, M. Sri Lakshmi, Srinivasa Rao Madala, P.V.V.S.D Nagendrudu, Buradagunta Swathi Sri, Naresh Kumar Bhagavatham

10.14445/23488549/IJECE-V13I1P110

How to Cite?

Giribabu Sadineni, M. Sri Lakshmi, Srinivasa Rao Madala, P.V.V.S.D Nagendrudu, Buradagunta Swathi Sri, Naresh Kumar Bhagavatham, "Integrating Reinforcement Learning and Contextual Analysis for Enhanced Pedestrian Behavior Prediction," SSRG International Journal of Electronics and Communication Engineering, vol. 13,  no. 1, pp. 107-126, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I1P110

Abstract:

The rapid growth and complexity of urban traffic necessitate advanced solutions to enhance pedestrian safety, particularly at zebra crossings. This research presents a comprehensive framework that integrates Reinforcement Learning (RL) and context-aware prediction to achieve accurate, real-time pedestrian behavior forecasting. The proposed model is dynamic as it uses deep learning solutions, such as Deep Q-Learning (DQN) and Proximal Policy Optimization (PPO), and adjusts to the changing urban environment based on the given context of the time of the day, weather conditions, and traffic lights. Evaluation of the model based on the Pedestrian Intention Estimation (PIE) dataset shows that the developments of the model have strong performance, with an accuracy of 92.5, a precision of 91.1, a recall of 90.3, and an F1 score of 90.7. It performs better than the current models regarding computational efficiency and faster inferences for real-time deployment with reduced memory consumption. Adaptive learning in constant updates and smooth integration with the traffic management systems towards proactive safety approaches are primary contributions. The framework, in addition to improving traffic safety in the cities and aiding in the development of autonomous vehicle systems, also identifies other areas for future research topics, such as controlling the unpredictable actions of pedestrians and managing consumption rate aspects at a larger scale of challenges.

Keywords:

Pedestrian behavior prediction, Reinforcement learning, Context-aware prediction, Urban traffic safety, Real-time analysis, Autonomous vehicles.

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