Enhanced free space detection in multiple lanes based on single CNN with scene identification

Many systems for autonomous vehicles' navigation rely on lane detection. Traditional algorithms usually estimate only the position of the lanes on the road, but an autonomous control system may also need to know if a lane marking can be crossed or not, and what portion of space inside the lane is free from obstacles, to make safer control decisions. On the other hand, free space detection algorithms only detect navigable areas, without information about lanes. State-of-the-art algorithms use CNNs for both tasks, with significant consumption of computing resources. We propose a novel approach that estimates the free space inside each lane, with a single CNN. Additionally, adding only a small requirement concerning GPU RAM, we infer the road type, that will be useful for path planning. To achieve this result, we train a multi-task CNN. Then, we further elaborate the output of the network, to extract polygons that can be effectively used in navigation control. Finally, we provide a computationally efficient implementation, based on ROS, that can be executed in real time. Our code and trained models are available online.Comment: Will appear in the 2019 IEEE Intelligent Vehicles Symposium (IV 2019

The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping

Many tasks performed by autonomous vehicles such as road marking detection, object tracking, and path planning are simpler in bird's-eye view. Hence, Inverse Perspective Mapping (IPM) is often applied to remove the perspective effect from a vehicle's front-facing camera and to remap its images into a 2D domain, resulting in a top-down view. Unfortunately, however, this leads to unnatural blurring and stretching of objects at further distance, due to the resolution of the camera, limiting applicability. In this paper, we present an adversarial learning approach for generating a significantly improved IPM from a single camera image in real time. The generated bird's-eye-view images contain sharper features (e.g. road markings) and a more homogeneous illumination, while (dynamic) objects are automatically removed from the scene, thus revealing the underlying road layout in an improved fashion. We demonstrate our framework using real-world data from the Oxford RobotCar Dataset and show that scene understanding tasks directly benefit from our boosted IPM approach.Comment: equal contribution of first two authors, 8 full pages, 6 figures, accepted at IV 201

Object detection, distributed cloud computing and parallelization techniques for autonomous driving systems.

Autonomous vehicles are increasingly becoming a necessary trend towards building the smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection, amongst others. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that also considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper reviews the latest techniques towards creating our own end-to-end autonomous vehicle system, considering the state-of-the-art methods on object detection, and the possible incorporation of distributed systems and parallelization to deploy these methods. Our findings show that while techniques such as convolutional neural networks, recurrent neural networks, and long short-term memory can effectively handle the initial detection and path planning tasks, more efforts are required to implement cloud computing to reduce the computational time that these methods demand. Additionally, we have mapped different strategies to handle the parallelization task, both within and between the networks

Detection-segmentation convolutional neural network for autonomous vehicle perception

Object detection and segmentation are two core modules of an autonomous vehicle perception system. They should have high efficiency and low latency while reducing computational complexity. Currently, the most commonly used algorithms are based on deep neural networks, which guarantee high efficiency but require high-performance computing platforms. In the case of autonomous vehicles, i.e. cars, but also drones, it is necessary to use embedded platforms with limited computing power, which makes it difficult to meet the requirements described above. A reduction in the complexity of the network can be achieved by using an appropriate: architecture, representation (reduced numerical precision, quantisation, pruning), and computing platform. In this paper, we focus on the first factor - the use of so-called detection-segmentation networks as a component of a perception system. We considered the task of segmenting the drivable area and road markings in combination with the detection of selected objects (pedestrians, traffic lights, and obstacles). We compared the performance of three different architectures described in the literature: MultiTask V3, HybridNets, and YOLOP. We conducted the experiments on a custom dataset consisting of approximately 500 images of the drivable area and lane markings, and 250 images of detected objects. Of the three methods analysed, MultiTask V3 proved to be the best, achieving 99% mAP_50 for detection, 97% MIoU for drivable area segmentation, and 91% MIoU for lane segmentation, as well as 124 fps on the RTX 3060 graphics card. This architecture is a good solution for embedded perception systems for autonomous vehicles. The code is available at: https://github.com/vision-agh/MMAR_2023.Comment: The paper was accepted for the MMAR 2023 conference (27th International Conference on Methods and Models in Automation and Robotics

Detection of 3D Object in Point Cloud: Cloud Semantic Segmentation in Lane Marking

Managing a city efficiently and effectively is more important than ever as growing population and economic strain put a strain on infrastructure like transportation and public services like keeping urban green areas clean and maintained. For effective administration, knowledge of the urban setting is essential. Both portable and stationary laser scanners generate 3D point clouds that accurately depict the environment. These data points may be used to infer the state of the roads, buildings, trees, and other important elements involved in this decision-making process. Perhaps they would support "smart" or "smarter" cities in general. Unfortunately, the point clouds do not immediately supply this sort of data. It must be eliminated. This extraction is done either by human specialists or by sophisticated computer programmes that can identify objects. Because the point clouds might represent such large locations, relying on specialists to identify the things may be an unproductive use of time (streets or even whole cities). Automatic or nearly automatic discovery and recognition of essential objects is now possible with the help of object identification software. In this research, In this paper, we describe a unique approach to semantic segmentation of point clouds, based on the usage of contextual point representations to take use of both local and global features within the point cloud. We improve the accuracy of the point's representation by performing a single innovative gated fusion on the point and its neighbours, which incorporates the knowledge from both sets of data and enhances the representation of the point. Following this, we offer a new graph point net module that further develops the improved representation by composing and updating each point's representation inside the local point cloud structure using the graph attention block in real time. Finally, we make advantage of the global structure of the point cloud by using spatial- and channel-wise attention techniques to construct the ensuing semantic label for each point

Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planning

Growing interest in autonomous driving (AD) and intelligent vehicles (IVs) is fueled by their promise for enhanced safety, efficiency, and economic benefits. While previous surveys have captured progress in this field, a comprehensive and forward-looking summary is needed. Our work fills this gap through three distinct articles. The first part, a "Survey of Surveys" (SoS), outlines the history, surveys, ethics, and future directions of AD and IV technologies. The second part, "Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors" delves into the development of control, computing system, communication, HD map, testing, and human behaviors in IVs. This part, the third part, reviews perception and planning in the context of IVs. Aiming to provide a comprehensive overview of the latest advancements in AD and IVs, this work caters to both newcomers and seasoned researchers. By integrating the SoS and Part I, we offer unique insights and strive to serve as a bridge between past achievements and future possibilities in this dynamic field.Comment: 17pages, 6figures. IEEE Transactions on Systems, Man, and Cybernetics: System