Work

A Framework for Assessment of Perception Systems in Autonomous Vehicles

Year: 2025

Type: article

Abstract: Sensors are integral components of autonomous vehicles and serve as vital interfaces that allow vehicles to perceive and interact with their surroundings. Although existing research has focused primarily on sensors in specific road settings, limited attention has been paid to a standardized methodology to understand their efficacy in urban and unconventional environments. This study focuses on common sensor types and their performance in urban terrains with an outlook for unconventional terrains, bridging a critical gap in the literature. A framework is proposed to quantify sensor performance in various environments, using experiments and simulations for data acquisition. The framework facilitates precise analysis, enabling researchers and developers to effectively evaluate sensor capabilities. To validate this framework, a simulated scenario was employed using Carla to observe the behavior of an autonomous vehicle. The results demonstrate that the sensors with the respective algorithms in the simulation are adapted to different lighting conditions, ensuring consistent sensory performance. The framework enables robust localization and kinematic estimations, enhancing the analysis of vehicle motion using state-of-the-art simulators. This research contributes to a deeper understanding of sensor performance and a validated framework to assess this performance, offering a valuable tool for advancing autonomous vehicle technology.

Source: IEEE Access

DOI: 10.1109/access.2025.3582681

Authors: Hussein Hennaoui, Daniel Paluszczyszyn, Lipika Deka, Serhan Cosar

Institution: De Montfort University

Language: English

Topic: Autonomous Vehicle Technology and Safety

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,075