Research in Autonomous Driving is taking momentum due to the inherent advantages of autonomous driving systems. The main advantage being the disassociation of the driver from the vehicle reducing the human intervention. However, the Autonomous Driving System involves many subsystems which need to be integrated as a whole system. Some of the tasks include Motion Planning, Vehicle Localization, Pedestrian Detection, Traffic Sign Detection, Road-marking Detection, Automated Parking, Vehicle Cybersecurity, and System Fault Diagnosis. This paper aims to the overview of various Machine Learning and Deep Learning Algorithms used in Autonomous Driving Architectures for different tasks like Motion Planning, Vehicle Localization, Pedestrian Detection, Traffic Sign Detection, Road-marking Detection, Automated Parking, Vehicle Cybersecurity and Fault Diagnosis. This paper surveys the technical aspects of Machine Learning and Deep Learning Algorithms used for Autonomous Driving Systems. Comparison of these algorithms is done based on the metrics like mean Intersect in over Union (mIoU), Average Precision (AP)missed detection rate, miss rate False Positives Per Image (FPPI), and average number for false frame detection. This study contributes to picture a review of the Machine Learning and Deep Learning Algorithms used for Autonomous Driving Systems and is organized based on the different tasks of the system.

Autonomous Driving Architectures: Insights of Machine Learning and Deep Learning Algorithms

Due to the complex visual environment and incomplete display of parking slots on around-view images, vision-based parking slot detection is a major challenge. Previous studies in this field mostly use the existing models to solve the problem, the steps of which are cumbersome. In this paper, we propose a parking slot detection method that uses directional entrance line regression and classification based on a deep convolutional neural network (DCNN) to make it robust and simple. For parking slots with different shapes and observed from different angles, we represent the parking slot as a directional entrance line. Subsequently, we design a DCNN detector to simultaneously obtain the type, position, length, and direction of the entrance line. After that, the complete parking slot can be easily inferred using the detection results and prior geometric information. To verify our method, we conduct experiments on the public ps2.0 dataset and self-annotated parking slot dataset with 2,135 images. The results show that our method not only outperforms state-of-the-art competitors with a precision rate of 99.68% and a recall rate of 99.41% on the ps2.0 dataset but also performs a satisfying generalization on the self-annotated dataset. Moreover, it achieves a real-time detection speed of 13 ms per frame on Titan Xp. By converting the parking slot into a directional entrance line, the specially designed DCNN detector can quickly and effectively detect various types of parking slots.

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Parking Slot Detection on Around-View Images Using DCNN.

Deep learning has recently demonstrated its promising performance for vision-based parking-slot detection. However, very few existing methods explicitly take into account learning the link information of the marking-points, resulting in complex post-processing and erroneous detection. In this letter, we propose an attentional graph neural network based parking-slot detection method, which refers the marking-points in an around-view image as graph-structured data and utilize graph neural network to aggregate the neighboring information between marking-points. Without any manually designed post-processing, the proposed method is end-to-end trainable. Extensive experiments have been conducted on public benchmark dataset, where the proposed method achieves state-of-the-art accuracy. Code is publicly available at https://github.com/Jiaolong/gcn-parking-slot .

Attentional Graph Neural Network for Parking-Slot Detection

The autonomous parking of vehicles requires the ability to accurately locate an available parking slot in the vicinity of a vehicle. Since parking slots have a variety of shapes and colors, may be occluded by obstacles, or look different due to surroundings such as lighting, accurately locating them can be a challenging task. In this paper, we propose a context-based parking slot detection method inspired by the process of a human driver finding a parking slot. Our method consists of two deep network modules: a parking context recognizer and parking slot detector . The parking context recognizer identifies the parking environment (type, angle, and availability of a parking slot), whereas the parking slot detector locates the exact position of a parking slot by multiple type-based fine-tuned detectors with rotated anchor boxes and a rotated non-maximal suppression. In addition, we release a realistic parking slot dataset, which comprises 22817 images of parking slots having various attributes and external conditions. We also propose a new evaluation metric for parking slot detection, reflecting whether a vehicle can be parked within the detected parking slot. Through comparison and ablation study in experiments, we demonstrate that our method outperformed the previous deep-learning-based methods, along with having a short operation time. The source codes and the dataset are available at https://github.com/dohoseok/context-based-parking-slot-detect/ .

Context-Based Parking Slot Detection With a Realistic Dataset

In the field of autonomous vehicles, various heterogeneous sensors, such as LiDAR, Radar, camera, etc, are combined to improve the vehicle ability of sensing accuracy and robustness. Multi-modal perception and learning has been proved to be an effective method to help vehicle understand the nature of complex environments. Event camera is a bio-inspired vision sensor that captures dynamic changes in the scene and filters out redundant information with high temporal resolution and high dynamic range. These characteristics of the event camera make it have a certain application potential in the field of autonomous vehicles. In this paper, we introduce a fully convolutional neural network with feature attention gate component (FAGC) for vehicle detection by combining frame-based and event-based vision. Both grayscale features and event features are fed into the feature attention gate component (FAGC) to generate the pixel-level attention feature coefficients to improve the feature discrimination ability of the network. Moreover, we explore the influence of different fusion strategies on the detection capability of the network. Experimental results demonstrate that our fusion method achieves the best detection accuracy and exceeds the accuracy of the method that only takes single-mode signal as input.

Fusion-Based Feature Attention Gate Component for Vehicle Detection Based on Event Camera

Due to the complex visual environment, such as lighting variations, shadows, and limitations of vision, the accuracy of vacant parking slot detection for the park assist system (PAS) with a standalone around view monitor (AVM) needs to be improved. To address this problem, we propose a vacant parking slot detection method based on deep learning, namely VPS-Net. VPS-Net converts the vacant parking slot detection into a two-step problem, including parking slot detection and occupancy classification. In the parking slot detection stage, we propose a parking slot detection method based on YOLOv3, which combines the classification of the parking slot with the localization of marking points so that various parking slots can be directly inferred using geometric cues. In the occupancy classification stage, we design a customized network whose size of convolution kernel and number of layers are adjusted according to the characteristics of the parking slot. Experiments show that VPS-Net can detect various vacant parking slots with a precision rate of 99.63% and a recall rate of 99.31% in the ps2.0 dataset, and has a satisfying generalizability in the PSV dataset. By introducing a multi-object detection network and a classification network, VPS-Net can detect various vacant parking slots robustly.

Vacant Parking Slot Detection in the Around View Image Based on Deep Learning

Due to distortion, limitations of vision, and occlusion, most of the existing vacant parking slot detection methods with a standalone around view monitor (AVM) are prone to miss some parking slots ...

Vacant parking slot detection and tracking during driving and parking with a standalone around view monitor

Linear feature extraction is crucial for special objects in semantic segmentation networks, such as slot marking and lanes. The objects with linear characteristics have global contextual information dependency. It is very difficult to capture the complete information of these objects in semantic segmentation tasks. To improve the linear feature extraction ability of the semantic segmentation network, we propose introducing the dilated convolution with vertical and horizontal kernels (DVH) into the task of feature extraction in semantic segmentation networks. Meanwhile, we figure out the outcome if we put the different vertical and horizontal kernels on different places in the semantic segmentation networks. Our networks are trained on the basis of the SS dataset, the TuSimple lane dataset and the Massachusetts Roads dataset. These datasets consist of slot marking, lanes, and road images. The research results show that our method improves the accuracy of the slot marking segmentation of the SS dataset by 2%. Compared with other state-of-the-art methods, our UnetDVH-Linear (v1) obtains better accuracy on the TuSimple Benchmark Lane Detection Challenge with a value of 97.53%. To prove the generalization of our models, road segmentation experiments were performed on aerial images. Without data argumentation, the segmentation accuracy of our model on the Massachusetts roads dataset is 95.3%. Moreover, our models perform better than other models when training with the same loss function and experimental settings. The experiment result shows that the dilated convolution with vertical and horizontal kernels will enhance the neural network on linear feature extraction.

https://www.mdpi.com/1424-8220/20/20/5759/pdf

UnetDVH-Linear: Linear Feature Segmentation by Dilated Convolution with Vertical and Horizontal Kernels.

Road garbage recognition and cleanliness assessment are very important for intelligent cleaning of vehicles. However, widely used garbage detection methods cannot provide accurate information to assess cleanliness. Also, only a few contributions are available in the road cleanliness assessment, and they too mainly depend on the category and quantity of garbage. In this paper, deep supervision UNet++ (DUNet++) is proposed to solve the problem of road garbage classification and segmentation, which can directly impact the output of the category of garbage and the occupied ground area. A new, simple and accurate method is established to assess road cleanliness by combining the results of garbage segmentation. Compared with the cleanliness index (CI), the road cleanliness index based on semantic segmentation (CISS) not only considers the road area occupied by different types or categories of the garbage but also considers the difficulty of cleaning them. A road garbage segmentation dataset, which consists of four categories (stones, leaves, sand, and bottles), is collected to train the designed garbage segmentation model, and it has achieved considerable garbage segmentation improvement with an MIoU (Mean Intersection over Union) of $76.73\pm 0.11$ . Especially when compared with the state-of-the-art methods, the model we designed has greatly improved the accuracy of garbage segmentation. Experimental results have shown that our road garbage segmentation and cleanliness assessment methods are relatively simple, and can provide accurate road cleanliness information for cleaning vehicles.

Wei Li

Papers

Vacant Parking Slot Detection in the Around View Image Based on Deep Learning

Parking Slot Detection on Around-View Images Using DCNN.

Vacant parking slot detection and tracking during driving and parking with a standalone around view monitor

UnetDVH-Linear: Linear Feature Segmentation by Dilated Convolution with Vertical and Horizontal Kernels.

Road Garbage Segmentation and Cleanliness Assessment Based on Semantic Segmentation Network for Cleaning Vehicles