Generalizing state-of-the-art object detectors for autonomous vehicles in unseen environments

Khosravian, Amir; Amirkhani, Abdollah; Kashiani, Hossein; Masih‐Tehrani, Masoud

doi:10.1016/j.eswa.2021.115417

Cited by 35 publications

(15 citation statements)

References 20 publications

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“…The open‐source software OSM (OpenStreetMap) sources the topographic maps of these two places. The second open‐source software SUMO (Simulation of Urban MObility), a realistic traffic generator helps in generating mobility of vehicles for road network and route visualization 42 . We have chosen different types of location for diversity.…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence‐empowered vision‐based self driver assistance system for internet of autonomous vehicles

Rawlley

Gupta

2022

Trans Emerging Tel Tech

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Artificial intelligence (AI) and edge computing have truly advanced in vehicular networks encouraging assessment of real-time traffic conditions using kinetic information of autonomous vehicles with the help of road side units (RSUs). However, regardless of numerous improvements in sensor fusion technologies the existing vision/LIDAR-based systems have found severe difficulties during perception on roads. In addition, the inter-vehicular communications are hampered due to inefficient RSU placement techniques causing high-latency issues during transmission of messages. Therefore, this article presents an AI-driven vision-based self driver assistance system (VSDAS) comprising a joint RSU deployment mechanism that utilizes enhanced memetic architecture-based optimal RSU placement (MARP) method and an object detection model that implements an improved Haar-cascade object detection algorithm for speedy identification of object. We have designed two varieties of genetic algorithm (GA) to solve optimal placement problem of RSUs: genetic architecture-based with random restart hill climbing (GARRH) and the other is MARP for efficient placement of RSUs. After our experimental results, we see that the MARP algorithm shows best possible RSU locations over different generations achieving significantly better fitness scores than the GAHRC and GA ascribing to its local search process. In addition, Haar-cascade achieves better mean average precision than local binary pattern and histogram of oriented gradients by selecting key frames. The experimental outcomes of our model reveals that the proposed enhanced memetic algorithm reduces the transmission delay to a greater extent. Additionally, the outcomes of our investigations on two public datasets (KITTI and Panasonic) showed that our improved algorithm clearly enhances the object detection performance.

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Section: Methodsmentioning

confidence: 99%

Artificial intelligence‐empowered vision‐based self driver assistance system for internet of autonomous vehicles

Rawlley

Gupta

2022

Trans Emerging Tel Tech

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“…The use of autonomous vehicles, instead of vehicles with human drivers, can reduce human errors leading to improvement in overall performance and safety [52]. Nevertheless, autonomous vehicles may not make optimal decisions in all realworld environmental conditions, such as darkness, snowfall, and fog [53]. Hence, DL must address the challenge of object detection and classification, such as a reduced accuracy of detecting and classifying a diverse range of objects, such as cars, trucks, and empty spaces, under unpredictable realworld environments.…”

Section: Integration Of Future Generation Technologiesmentioning

confidence: 99%

The Role of Deep Learning in Parking Space Identification and燩rediction燬ystems

Rasheed¹,

Saleem²,

Yau³

et al. 2023

Computers, Materials &Amp; Continua

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In today's smart city transportation, traffic congestion is a vexing issue, and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40% of traffic congestion. Identifying parking spaces alone is insufficient because an identified available parking space may have been taken by another vehicle when it arrives, resulting in the driver's frustration and aggravating traffic jams while searching for another parking space. This explains the need to predict the availability of parking spaces. Recently, deep learning (DL) has been shown to facilitate drivers to find parking spaces efficiently, leading to a promising performance enhancement in parking identification and prediction systems. However, no work reviews DL approaches applied to solve parking identification and prediction problems. Inspired by this gap, the purpose of this work is to investigate, highlight, and report on recent advances in DL approaches applied to predict and identify the availability of parking spaces. A taxonomy of DL-based parking identification and prediction systems is established as a methodology by classifying and categorizing existing literature, and by doing so, the salient and supportive features of different DL techniques for providing parking solutions are presented. Moreover, several open research challenges are outlined. This work identifies that there are various DL architectures, datasets, and performance measures used to address parking identification and prediction problems. Moreover, there are some open-source implementations available that can be used directly either to extend existing works or explore a new domain. This is the first short survey article that focuses on the use of DL-based techniques in parking identification and prediction systems for smart cities. This study concludes that although the deployment of DL in parking identification and prediction systems provides various benefits, the convergence of these two types of systems and DL brings about new issues that must be resolved in the near future.

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“…However, the traditional method of generating real-world data for autonomous driving requires an enormous amount of time and cost for data collection and labeling. As a consequence, it is required to solve the problems in autonomous driving applications without generating additional real-world data-sets [34]- [36].…”

Section: Introductionmentioning

confidence: 99%

Fast and Lite Point Cloud Semantic Segmentation for Autonomous Driving Utilizing LiDAR Synthetic Training Data

et al. 2022

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In the autonomous car, perception with point cloud semantic segmentation helps obtain a wealth of information about the surrounding road environment. Despite the massive progress of recent researches, the existing machine learning networks are still insufficient for online applications of autonomous driving due to too subdivided classes, the lack of training data, and their heavy computing load. To solve these problems, we propose a fast and lite point cloud semantic segmentation network for autonomous driving, which utilizes LiDAR synthetic data to improve the performance by transfer learning. First, we modify the labeling classes and generate the LiDAR synthetic data-set for additional training to alleviate the lack of training data of the realistic data-set. Then, to lower the computing load, we adopt a projection-based method and apply a lightweight segmentation network to projected LiDAR images, which has drastically reduced computing. Finally, we verified and evaluated the proposed network in this paper through experiments. Experimental results show that the proposed network can perform the threedimensional point cloud semantic segmentation in an online way, in which the inference speed overwhelms the existing algorithms.

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Generalizing state-of-the-art object detectors for autonomous vehicles in unseen environments

Cited by 35 publications

References 20 publications

Artificial intelligence‐empowered vision‐based self driver assistance system for internet of autonomous vehicles

Artificial intelligence‐empowered vision‐based self driver assistance system for internet of autonomous vehicles

The Role of Deep Learning in Parking Space Identification and燩rediction燬ystems

Fast and Lite Point Cloud Semantic Segmentation for Autonomous Driving Utilizing LiDAR Synthetic Training Data

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