Towards a Context-Aware Driving Assistance System (CA-DAS): Advancing Intelligent Vehicular Safety through Multimodal Context Integration

Document Type

Conference Proceeding

Source of Publication

Transportation Research Procedia

Publication Date

1-1-2026

Abstract

Driving-related behavioural factors are responsible for 90% of traffic collisions. The rapid growth of urbanization and the complexity of the traffic conditions demand a smart, efficient, and flexible transportation system. The advancement of transportation through technologies such as the Internet of Things (IoT) and AI have reshaped the way that drivers interact with their vehicles and the surrounding environment. In this paper, we propose a comprehensive Context-Aware Driving Assistance System (CA-DAS) that employs sensor fusion, semantic context modelling, along with a machine-learning-based approach to provide personalised and proactive driving assistance across dynamic scenarios. The proposed CA-ADS was developed using a Robot Operating System (ROS) architecture and examined using the CARLA simulator across 150 heterogeneous scenarios, including rural, urban, and diverse driver state conditions. Furthermore, we conducted a Likert-scale survey with 20 drivers to measure their satisfaction with the system. The finding demonstrated the effectiveness of the system as a proactive solution for enhancing safety. The results show that the proposed CA-DAS achieved over 94 % accuracy in risk detection, reduced the average time to warning by approximately 28%, and decreased lane changes under driver conditions such as fatigue by about 32%. The results also indicated strong driver acceptance and trust with satisfaction averaged 4.6 out of 5. Our study contributes to the field by developing a scalable and flexible framework for a Context-Aware driving assistance system in semi-autonomous and human-driven vehicles.

ISSN

2352-1457

Publisher

Elsevier BV

Volume

97

First Page

126

Last Page

132

Disciplines

Computer Sciences

Keywords

Human-Centered AI, Multimodal Context Modeling, Proactive Safety Systems, Sensor Fusion

Scopus ID

105041998651

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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