Abstmct-In this correspondence, we show how to recover the motion of an observer relative to a planar surface from image brightness derivatives. We do not compute the optical flow as an intermediate step, only the spatial and temporal brightness gradients (at a minimum of eight points). We first present two iterative schemes for solving nine nonlinear equations in terms of the motion and surface parameters that are derived from a least-squares fomulation. An initial pass over the relevant image region is wed to accumulate a number of moments of the image brightness derivatives. All of the quantities used in the iteration are efficiently computed from these totals without the need to refer back to the image. We then show that either of two possible solutions can be obtained in closed form. We first solve a linear matrix equation for the elements of a 3 x 3 matrix. The eigenvalue decomposition of the symmetric part of the matrix is then used to compute the motion parameters and the plane orientation. A new compact notation allows us to show easily that there are at most two planar solutions.

IEEE Transactions on Pattern Analysis and Machine Intelligence

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks, lack of systematic summary and research directions in the future. Here we propose a Survey of Surveys (SoS) for total technologies of AD and IVs that reviews the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. To our knowledge, this article is the first SoS with milestones in AD and IVs, which constitutes our complete research work together with two other technical surveys. We anticipate that this article will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future. 

Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks, lack of systematic summary and research directions in the future. Here we propose a Survey of Surveys (SoS) for total technologies of AD and IVs that reviews the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. To our knowledge, this article is the first SoS with milestones in AD and IVs, which constitutes our complete research work together with two other technical surveys. We anticipate that this article will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future.

https://dspace.lib.cranfield.ac.uk/bitstream/1826/18847/1/Milestones_in_autonomous_driving-2022.pdf

In this paper, a novel integrated real-time trajectory planning and tracking control framework capable of dealing with autonomous ground vehicle (AGV) parking maneuver problems is presented. In the motion planning component, a newly-proposed idea of utilizing deep neural networks (DNNs) for approximating optimal parking trajectories is further extended by taking advantages of a recurrent network structure. The main aim is to fully exploit the inherent relationships between different vehicle states in the training process. Furthermore, two transfer learning strategies are applied such that the developed motion planner can be adapted to suit various AGVs. In order to follow the planned maneuver trajectory, an adaptive learning tracking control algorithm is designed and served as the motion controller. By adapting the network parameters, the stability of the proposed control scheme, along with the convergence of tracking errors, can be theoretically guaranteed. In order to validate the effectiveness and emphasize key features of our proposal, a number of experimental studies and comparative analysis were executed. The obtained results reveal that the proposed strategy can enable the AGV to fulfill the parking mission with enhanced motion planning and control performance. Note to Practitioners&#x2014;This article was motivated by the problem of optimal automatic parking planning and tracking control for autonomous ground vehicles (AGVs) maneuvering in a restricted environment (e.g., constrained parking regions). A number of challenges may arise when dealing with this problem (e.g., the model uncertainties involved in the vehicle dynamics, system variable limits, and the presence of external disturbances). Existing approaches to address such a problem usually exploit the merit of optimization-based planning/control techniques such as model predictive control and dynamic programming in order for an optimal solution. However, two practical issues may require further considerations: 1). The nonlinear (re)optimization process tends to consume a large amount of computing power and it might not be affordable in real-time; 2). Existing motion planning and control algorithms might not be easily adapted to suit various types of AGVs. To overcome the aforementioned issues, we present an idea of utilizing the recurrent deep neural network (RDNN) for planning optimal parking maneuver trajectories and an adaptive learning NN-based (ALNN) control scheme for robust trajectory tracking. In addition, by introducing two transfer learning strategies, the proposed RDNN motion planner can be adapted to suit different AGVs. In our follow-up research, we will explore the possibility of extending the developed methodology for large-scale AGV parking systems collaboratively operating in a more complex cluttered environment.

/pdf/deep-learning-based-trajectory-planning-and-control-for-d7a9qfg6.pdf

Deep Learning-Based Trajectory Planning and Control for Autonomous Ground Vehicle Parking Maneuver

This article is concerned with the problem of planning optimal maneuver trajectories and guiding the mobile robot toward target positions in uncertain environments for exploration purposes. A hierarchical deep learning-based control framework is proposed which consists of an upper level motion planning layer and a lower level waypoint tracking layer. In the motion planning phase, a recurrent deep neural network (RDNN)-based algorithm is adopted to predict the optimal maneuver profiles for the mobile robot. This approach is built upon a recently proposed idea of using deep neural networks (DNNs) to approximate the optimal motion trajectories, which has been validated that a fast approximation performance can be achieved. To further enhance the network prediction performance, a recurrent network model capable of fully exploiting the inherent relationship between preoptimized system state and control pairs is advocated. In the lower level, a deep reinforcement learning (DRL)-based collision-free control algorithm is established to achieve the waypoint tracking task in an uncertain environment (e.g., the existence of unexpected obstacles). Since this approach allows the control policy to directly learn from human demonstration data, the time required by the training process can be significantly reduced. Moreover, a noisy prioritized experience replay (PER) algorithm is proposed to improve the exploring rate of control policy. The effectiveness of applying the proposed deep learning-based control is validated by executing a number of simulation and experimental case studies. The simulation result shows that the proposed DRL method outperforms the vanilla PER algorithm in terms of training speed. Experimental videos are also uploaded, and the corresponding results confirm that the proposed strategy is able to fulfill the autonomous exploration mission with improved motion planning performance, enhanced collision avoidance ability, and less training time. 

/pdf/design-and-experimental-validation-of-deep-reinforcement-q8wy47t0.pdf

Design and Experimental Validation of Deep Reinforcement Learning-Based Fast Trajectory Planning and Control for Mobile Robot in Unknown Environment

Lane detection and tracking has been an active research area in the past twenty years mainly for the driver assistance application. Due to the large variations of traffic scenes and illumination conditions, this problem causes the usage of diverse approaches and sensing modalities. In this paper, we review the vision-based lane detection and tracking methods complemented with other sensor information when necessary. Approaches that adopt conventional computer vision techniques are reviewed and compared according to the separate functional modules in a generic framework. The recently developed machine learning especially deep learning based methods in the limited literature are analysed and also discussed, demonstrating high potential in the current and future challenging lane perception scenarios. While impressive achievements have been demonstrated under limited scenarios, new ideas and approaches are still desired such that the next generation robust and efficient system can be built in the service of autonomous vehicles.

Vision-based lane detection and tracking for driver assistance systems: A survey

Pose estimation with state or measurement constraints has been frequent in autonomous vehicle navigation. Aiming at incorporating constraints inherently, this article proposes a dynamic potential field (DPF)-based formulation to represent states, measurements, and constraints on connected Riemannian manifolds. The state equation and the output equation are derived in the DPF forms, which imply the probabilistic inference with states and measurements. Constraints are incorporated by projecting points toward a constraint subset in the state space and the measurement space, where the DPF representing constraints is created. An information fusion scheme has been designed for DPFs, which are obtained from multisensor measurements and constraints. KITTI and self-collected sequences have been used in experiments, during which it is observed that the rotational drift is corrected and translational errors are reduced, thanks to the fusion of stereo visual odometry (SVO), heading measurements, and road maps. Note to Practitioners —Pose estimation aims to obtain ground vehicle’s position and heading in a certain coordinate system. The multisensor fusion has been a popular solution in reducing estimation errors, and constraints are helpful in improving the pose estimation performance by limiting possible solutions. This article proposes a framework to model multisensor pose estimation problems with constraints. The approximated estimation scheme inspired by the Monte Carlo method has been provided such that the framework can be applied to various systems without analytic modeling. Experiments in KITTI and self-collected sequences have been conducted to demonstrate the superiority of the proposed approach by fusing stereo visual odometry (SVO) and heading measurements under road constraints. The proposed approach can be ameliorated by adding an absolute reference in translation to correct the inherent translational drift in VO.

Road-Constrained Geometric Pose Estimation for Ground Vehicles

Conventional SLAM algorithms takes a strong assumption of scene motionlessness, which limits the application in real environments. This paper tries to tackle the challenging visual SLAM issue of complicated environments. We present GMC, grid-based motion clustering approach, a lightweight dynamic object filtering method that is free from high-power and expensive processors and is able to differentiate moving objects out of the surroundings. GMC encapsulates motion consistency as the statistical likelihood of detected key points within a certain region. Using this method can we provide real-time and robust correspondence algorithm that can differentiate dynamic objects with static backgrounds. Furthermore, we evaluate our system in the public TUM dataset. To compare with the state-of-the-art methods, our system can provide more accurate results by detecting dynamic objects.

/pdf/gmc-grid-based-motion-clustering-in-dynamic-environment-3dlby8xrxi.pdf

GMC: Grid Based Motion Clustering in Dynamic Environment

In this paper, we consider multi-lane detection in challenging urban scenarios such as emerging, ending, spitting and merging of lane markings, heavily curved lanes, zig-zag lanes, on/off ramp and disturbance of other road writings. We present a fast robust multi-lane detection and tracking framework to address these challenges. In this method, lane feature elements are first extracted and then grouped into clusters, and clusters are associated through energy minimization. Probabilistic decision making is adopted to track individual lane considering lane cluster measurements and prior lane state. A multi-lane tracking strategy is also presented to manage lane tracks from their appearance to disappearance, which can reduce false detection and improve robustness of the algorithm. Real driving data are used to verify the effectiveness of our algorithm in all mentioned challenging scenarios.

/pdf/real-time-robust-multi-lane-detection-and-tracking-in-3eqpq9hai0.pdf

Real-time Robust Multi-lane Detection and Tracking in Challenging Urban Scenarios

Accurate and robust detection of lane and curb in urban areas is essential to many real-world intelligent vehicle applications. Existing vision-based researches treat lane detection and curb detection separately due to the nature of curb detection problem relying on 3D features, which is not efficient when driving in a real world. This paper presents a unified network to incorporate these two tasks together by taking advantage of the powerful feature learning ability brought by deep convolutional neural networks. The resulting unified network provides valuable road boundary information by curb detection even when lane markings are not visible during vehicle navigation. Another significant capability coming with the proposed method is able to accurately differentiate various lane and curb instances with tiny gaps or complex spatial relationships which are thought as the biggest challenge in real driving situations. To achieve this, the network is specially designed to guide lane and curb instances to learnable kernel through pixel grouping. This learnable kernel is capable to handle any number of arbitrary-shaped lanes and curbs no matter which angle the vehicle is heading at. In the end, the presented approach is evaluated on two datasets (BDD100K and self-collected dataset) scoring 32 FPS processing speed. Results are very encouraging with more than 98% F 1 measure for both lane and curb detection on the self-collected dataset.

/pdf/lacnet-real-time-end-to-end-arbitrary-shaped-lane-and-curb-2x8ss5eyg0.pdf

LaCNet: Real-time End-to-End Arbitrary-shaped Lane and Curb Detection with Instance Segmentation Network

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