Autonomous vehicle refers to a vehicle capable of perceiving its surrounding environment and driving with little or no human driver input. The perception system is a fundamental component which enables the autonomous vehicle to collect data and extract relevant information from the environment to drive safely. Benefit from the recent advances in computer vision, the perception task can be achieved by using sensors, such as camera, LiDAR, radar, and ultrasonic sensor. This paper reviews publications on computer vision and autonomous driving that are published during the last ten years. In particular, we first investigate the development of autonomous driving systems and summarize these systems that are developed by the major automotive manufacturers from different countries. Second, we investigate the sensors and benchmark data sets that are commonly utilized for autonomous driving. Then, a comprehensive overview of computer vision applications for autonomous driving such as depth estimation, object detection, lane detection, and traffic sign recognition are discussed. Additionally, we review public opinions and concerns on autonomous vehicles. Based on the discussion, we analyze the current technological challenges that autonomous vehicles meet with. Finally, we present our insights and point out some promising directions for future research. This paper will help the reader to understand autonomous vehicles from the perspectives of academia and industry.

Applications of Computer Vision in Autonomous Vehicles: Methods, Challenges and Future Directions

The Traffic Sign Recognition (TSR) system is an essential component of Advanced Driver Assistance Systems (ADAS), which assists drivers in detecting and understanding traffic signs. However, recognizing traffic signs in complex situations, such as low or high lighting, adverse weather conditions, and background colour, remains a challenging task. While numerous researchers have achieved high accuracy rates, they have encountered difficulties in attaining a balance between recognition accuracy and processing speed. To address these problem, this paper introduces a TSR algorithm that uses Convolutional Neural Networks (CNN) with enhanced padding functions to increase recognition accuracy. The algorithm initially preprocesses the input image by performing contrast enhancement and normalization, and then trains the CNN model on GTSRB, a vast dataset of traffic signs to extract relevant features and classify them into respective categories. The padding function expands the input image dimensions, allowing the model to extract intricate features of background scenarios, increasing recognition precision. The proposed algorithm was evaluated on traffic sign images from real-world scenarios, including low or high illumination and adverse weather conditions, demonstrating its ability to achieve a balance between recognition accuracy and processing time, potentially enhancing the safety and efficiency of ADAS systems by providing timely and accurate information to drivers.

A Traffic Sign Recognition Algorithm for ADAS based on CNN for Complex Scenarios

Real-time traffic light recognition is essential for autonomous driving. Yet, a cohesive overview of the underlying model architectures for this task is currently missing. In this work, we conduct a comprehensive survey and analysis of traffic light recognition methods that use convolutional neural networks (CNNs). We focus on two essential aspects: datasets and CNN architectures. Based on an underlying architecture, we cluster methods into three major groups: (1) modifications of generic object detectors which compensate for specific task characteristics, (2) multi-stage approaches involving both rule-based and CNN components, and (3) task-specific single-stage methods. We describe the most important works in each cluster, discuss the usage of the datasets, and identify research gaps.

pdf/traffic-light-recognition-using-convolutional-neural-p1yocc1xxk.pdf

Traffic Light Recognition using Convolutional Neural Networks: A Survey

Traffic signs are essential for road safety, guiding and informing drivers about rules and conditions. Ignoring signs or traffic rules can lead to accidents, causing property damage, and even deaths. To ensure safety, drivers must remain aware of traffic signs, obey their instructions, and drive responsibly. Notably, Sri Lanka has unique traffic signs compared to other countries. This study aims to develop a deep learning-based traffic sign alert system with good accuracy and efficiency in traffic sign recognition and enhance driver awareness by integrating an auditory alert system. This alert system was implemented under two main sections: object detection system and auditory alert system. The traffic signs dataset images were labeled and pre-processed using the Roboflow software tool. The You Only Look Once version 5 (YOLOv5) model was used to train the system, which allowed it to understand complicated patterns and attributes associated with 15 different sign classes. To improve driver awareness, an auditory alert system was developed using the PyDub library to generate real-time alerts in the form of voice messages. These alerts are triggered whenever a traffic sign is detected, giving an additional layer of information to the driver. On the test dataset, the integrated and fine-tuned YOLOv5 model achieved the F1 score of 90.09% and mean average precision (mAP) of 87.55% resulting in good evaluation metrics values for detection. This YOLOv5 model-based traffic sign auditory alert system was highly effective and efficient in increasing driver awareness and also helped to reduce road accidents. 

Traffic Sign Recognition and Auditory Alert System for Sri Lankan Drivers Using Deep-Learning

Automotive Driver Assistance Systems (ADAS) applications are currently an intensive field of study and innovation. The development of an ADAS is a multidisciplinary task involving electronic hardware design, advanced software implementation, safety considerations and many more. Building an ADAS application implies some challenges that are addressed in this paper. Firstly, all ADAS applications run on highly specific hardware devices embedded in the car with limited computation resources. In this work a novel embedded platform, iADASys, is developed and tested. The platform integrates the elements required to implement an artificial vision based ADAS application such as high performance processor with Deep Learning (DL) computation co-processors or multi-channel high resolution video streaming hardware. Secondly, this work implements an artificial vision application for traffic light detection based on deep neural networks. The model selected in this work is SSD_Mobilenet_V1 and it was trained using Bosch Small Traffic Light (BSTL) dataset. To fulfill real time requirement, the model image input resolution was maintained low at $300\times 300$ pixel. However, the small object size in the dataset together with low resolution lead to poor detection performance. This situation was addressed by fine tuning the model training hyperparameters related to detection scales and aspect ratios. Lastly, the model is deployed in the hardware platform and its performance is measured. Model inference is executed on a specialized mathematical co-processor obtaining the required real time response. The object detection performance is also measured, obtaining promising results.

Deep Learning-Based Traffic Light Detection in a Custom Embedded Hardware Platform for ADAS Applications

With increased travelling needs more than ever, traffic congestion has become a major concern in most urban areas. Allocating spaces for on-street parking, further hinders traffic flow, by limiting the effective road width available for driving. With the advancement of vehicle-to-infrastructure connectivity technologies, we explore how the impact of on-street parking on traffic congestion could be minimized, by dynamically configuring on-street parking spaces. Towards that end, we formulate dynamic on-street parking space configuration as an optimization problem, and we follow a data driven approach, considering the nature of our problem. Our proposed solution comprises a two-layer multi agent reinforcement learning based framework, which is inherently scalable to large road networks. The lane level agents are responsible for deciding the optimal parking space configuration for each lane, and we introduce a novel Deep Q-learning architecture which effectively utilizes long short term memory networks and graph attention networks to capture the spatio-temporal correlations evident in the given problem. The block level agents control the actions of the lane level agents and maintain a sufficient level of parking around the block. We conduct a set of comprehensive experiments using SUMO, on both synthetic data as well as real-world data from the city of Melbourne. Our experiments show that the proposed framework could reduce the average travel time loss of vehicles significantly, reaching upto 47%, with a negligible increase in the walking distance for parking.

https://export.arxiv.org/pdf/2512.02406v1.pdf

Dynamic Configuration of On-Street Parking Spaces using Multi Agent Reinforcement Learning

Recent work done on traffic sign and traffic light detection focus on improving detection accuracy in complex scenarios, yet many fail to deliver real-time performance, specifically with limited computational resources. In this work, we propose a simple deep learning based end-to-end detection framework, which effectively tackles challenges inherent to traffic sign and traffic light detection such as small size, large number of classes and complex road scenarios. We optimize the detection models using TensorRT and integrate with Robot Operating System to deploy on an Nvidia Jetson AGX Xavier as our embedded device. The overall system achieves a high inference speed of 63 frames per second, demonstrating the capability of our system to perform in real-time. Furthermore, we introduce CeyRo, which is the first ever large-scale traffic sign and traffic light detection dataset for the Sri Lankan context. Our dataset consists of 7984 total images with 10176 traffic sign and traffic light instances covering 70 traffic sign and 5 traffic light classes. The images have a high resolution of 1920 x 1080 and capture a wide range of challenging road scenarios with different weather and lighting conditions. Our work is publicly available at https://github.com/oshadajay/CeyRo.

Towards Real-time Traffic Sign and Traffic Light Detection on Embedded Systems

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Dynamic Configuration of On-Street Parking Spaces using Multi Agent Reinforcement Learning

Towards Real-time Traffic Sign and Traffic Light Detection on Embedded Systems