Multi-lane Detection Based on RMFP for Autonomous Vehicle Navigation in Urban Environments

This paper presents a comprehensive study on the application of reinforcement learning (RL) techniques for autonomous vehicle navigation in complex urban environments. We propose a novel deep RL framework that combines state-of-the-art algorithms with realistic urban traffic simulations to train robust navigation policies. Our approach leverages a hierarchical learning structure to decompose the challenging urban driving task into more manageable sub-tasks. Extensive experiments in simulated urban scenarios demonstrate that our method significantly outperforms baseline approaches in terms of safety, efficiency, and adaptability to diverse traffic conditions. We also conduct real-world validation tests to verify the transferability of learned policies to actual autonomous vehicles. Our results highlight the potential of RL-based techniques to enable safe and efficient autonomous navigation in dynamic city environments.

The robustness and stability of lane detection is vital for advanced driver assistance vehicle technology and even autonomous driving technology. To meet the challenges of real-time lane detection in complex traffic scenes, a simple but robust multilane detection method is proposed in this paper. The proposed method breaks down the lane detection task into two stages, that is, lane line detection algorithm based on instance segmentation and lane modeling algorithm based on adaptive perspective transform. Firstly, the lane line detection algorithm based on instance segmentation is decomposed into two tasks, and a multitask network based on MobileNet is designed. This algorithm includes two parts: lane line semantic segmentation branch and lane line Id embedding branch. The lane line semantic segmentation branch is mainly used to obtain the segmentation results of lane pixels and reconstruct the lane line binary image. The lane line Id embedding branch mainly determines which pixels belong to the same lane line, thereby classifying different lane lines into different categories and then clustering these different categories. Secondly, the adaptive perspective transformation model is adopted. In this model, the motion information is used to accurately convert the original image into a bird’s-eye view image, and then the least-squares second-order polynomial fitting is performed on the lane line pixels. Finally, experiments on the CULane dataset show that the proposed method achieved similar or better performance compared with several state-of-the-art methods, the F1 score of the proposed method in the normal test set and most challenge test sets is better than other algorithms, which verifies the effectiveness of the proposed method, and then the field experiments results show that the proposed method has good practical application value in various complex traffic scenes.

Autonomous navigation is a fundamental task in mobile robotics. In the last years, several approaches have been addressing the autonomous navigation in outdoor environments. Lately it has also been extended to robotic vehicles in urban environments. This paper focus in the road identification problem, which is an important capability to autonomous vehicle drive. Our approach is based on image processing, template matching classification, and finite state machines processing. The proposed system allows to train an image segmentation algorithm in order to identify navigable and non-navigable regions (inside/outside roads), generating as output the steering control for an Electric Autonomous Vehicle, that should stay following the road. Several experimental tests have been carried out under different environmental conditions to evaluate the proposed techniques.

Global navigation satellite systems (GNSS) are often integrated with inertial measurement units (IMU) for navigation in urban environments. The ability of these integrated systems to achieve precise positioning and navigation is highly dependent on accurate time synchronization. While the current time synchronization algorithms are capable of achieving millisecond-level synchronization in open environments, their performance deteriorates significantly in complex urban environments due to the impact on GNSS measurements of non-line-of-sight (NLOS) signals and multipath effects. Here we propose a loosely coupled GNSS/IMU integration time synchronization error modeling and compensation algorithm to tackle this problem in the context of urban vehicle navigation. In the algorithm, the time synchronization error is augmented as a state variable in the Robust Extended Kalman Filter (REKF), with the robustness factor threshold being dynamically adjusted based on the time synchronization error. Furthermore, we have designed a system time synchronization detection scheme based on two detection factors, with the aim of achieving high-precision GNSS/IMU time synchronization in complex urban environments. Experimental results show that the proposed algorithm synchronizes data time to the millisecond level in urban positioning environments. This represents an improvement of more than 90% in time synchronization precision compared to the traditional EKF-based time synchronization algorithm, and an improvement of more than 60% compared to the REKF-based time synchronization algorithm. Furthermore, compared to the EKF-based GNSS/IMU fusion algorithm, the proposed algorithm enhances the accuracy of the determination of both position and velocity in the horizontal and 3D directions by more than 50% and heading accuracy by 85%.

As is known to all, there are thousands of people died in traffic accidents in the world every year and 80% of the traffic accidents are due to the drivers' inattention. In order to reduce the occurrence of car accidents, the design of Cognitive Vehicle Safety (CVS) system has attracted wide attention. As an important functional system of the CVS system, Lane Departure Warning system's function is to warn the driver when the vehicle changes the lane without the driver's intention. In this paper, we present a method of lane detection which based on Lane geometric model and image connected region process. The model of the lane is produced by the projection of camera, which makes the parallel lane lines in real world become intersect. The lines on the same side of lane have the same geometric feature that can be used to build lane line geometric model for detection. The lane line can be classified as different lane line models by the geometric feature of it. In this paper, due to the different features of lane line in different images, the operation and the classification of modeling performs at the same time. But before that, we go through some process to pre-process the image, such as the image connected region process, morphology tophat filter, etc. The combination of these modules can overcome the universal lane detection problems, such as the road in different light conditions. Experimental results on real road will be presented to prove the effectiveness of the proposed lane detection algorithm.

A robust road markings detection algorithm is a fundamental component of intelligent vehicles' autonomous navigation in urban environment. This paper presents an algorithm for detecting road markings including zebra crossings, stop lines and lane markings to provide road information for intelligent vehicles. First, to eliminate the impact of the perspective effect, an Inverse Perspective Mapping (IPM) transformation is applied to the images grabbed by the camera; the region of interest (ROI) was extracted from IPM image by a low level processing. Then, different algorithms are adopted to extract zebra crossings, stop lines and lane markings. The experiments on a large number of street scenes in different conditions demonstrate the effectiveness of the proposed algorithm.

Vision-based bio-inspired control strategies offer great promise in demonstrating safe autonomous navigation of aerial microsystems in completely unstructured environments. This paper presents an innovative navigation technique that embeds bio-inspired wide-field processing of instantaneous optic flow patterns within the H∞ loop shaping synthesis framework, resulting in a dynamic controller that enables robust stabilization and command tracking behavior in obstacle-laden environments. The local environment is parameterized as a series of simpler corridor-like environments in the optic flow model, and the loop shaping controller is synthesized to provide robust stability across the range of modeled environments. Experimental validation is provided using a quadrotor aerial vehicle across environments with large variation in local structure, with the loop shaping controller demonstrating better tracking performance than other comparable controllers in straight-line corridors of different widths. The current approach is computationally efficient, as it does not involve explicit extraction of an environment depth map, and makes for an attractive paradigm for aerial microsystem navigation in urban environments.

LaneScanNET: A deep-learning approach for simultaneous detection of obstacle-lane states for autonomous driving systems

This paper deals with a problem of multitask agents' navigation in urban environment with obstacles using different algorithmic approaches: neural network models with a system of rewards and penalties, neural networks oriented on distance minimization, as well as models that use direct movement vectors and rectangular route structures. The novelty of the presented method is the creation of a hybrid algorithm that incorporates strict rules for movement in an initially unknown environment and, when obstacles appear, switches to a neural network for obstacle avoidance. This approach combines deterministic movement, which can be adapted to artificial intelligence for safe and reliable navigation in uncharted territory. Experiments showed that thirty neural argents trained purely using rewards and penalties in movement exhibits slow learning and cyclical movement as clusters, which is due to the fact that they optimize the current state without global planning. Models trained solely on distance optimization perform slightly better, but still create clusters of movement toward the goal. Meanwhile, the use of strict linearity rules and rectangular routes allows for effective obstacle avoidance and consistent target achievement with less time under conditions of limited graph information. The results show that rule-based learning yields better results in agent movement than fully autonomous agents. Consequently, the combined path generation method yields the best results in agent navigation along a known path.

<div class="section abstract"><div class="htmlview paragraph">Lane detection plays a critical role in autonomous vehicles for safe and reliable navigation. Lane detection is traditionally accomplished using a camera sensor and computer vision processing. The downside of this traditional technique is that it can be computationally intensive when high quality images at a fast frame rate are used and has reliability issues from occlusion such as, glare, shadows, active road construction, and more. This study addresses these issues by exploring alternative methods for lane detection in specific scenarios caused from road construction-induced lane shift and sun glare. Specifically, a U-Net, a convolutional network used for image segmentation, camera-based lane detection method is compared with a radar-based approach using a new type of sensor previously unused in the autonomous vehicle space: radar retro-reflectors. This evaluation is performed using ground truth data, obtained by measuring the lane positions and transforming them into pixel coordinates. The performance of each method is assessed using the statistical <i>R</i><sup>2</sup> score, indicating the correlation between the detected lane lines and the ground truth. The results show that the U-Net camera-based method exhibits limitations in accurately detecting and aligning the lane lines, particularly in challenging scenarios. However, the radar-based lane detection method demonstrates a strong correlation with the ground truth which implies that the use of this sensor may improve current reliability issues from conventional camera lane detection approach. Furthermore, the study highlights the limitations of the U-Net model for camera lane detection, especially in scenarios with sun glare. This study shows that infrastructure-based radar retro-reflectors can improve autonomous vehicle lane detection reliability. The integration of different sensor modalities and the development of advanced computer vision algorithms are crucial for improving the accuracy, reliability, and energy efficiency of lane detection systems. Addressing these challenges contributes to the advancement of autonomous vehicles and the realization of safer and more efficient transportation systems.</div></div>

<p>Pedestrian detection is one of the important features in autonomous ground vehicle (AGV). It ensures the capability for safety navigation in urban environment. Therefore, the detection accuracy became a crucial part which leads to implementation using Laser Range Finder (LRF) for better data representation. In this study, an improved laser configuration and fusion technique is introduced by implementation of triple LRFs in two layers with Pedestrian Data Analysis (PDA) to recognize multiple pedestrians. The PDA integrates various features from feature extraction process for all clusters and fusion of multiple layers for better recognition. The experiments were conducted in various occlusion scenarios such as intersection, closed-pedestrian and combine scenarios. The analysis of the laser fusion and PDA for all scenarios showed an improvement of detection where the pedestrians were represented by various detection categories which solve occlusion issues when low number<br />of laser data were obtained.</p>

GNSS receivers are designed to provide an estimate of three fundamental components for navigation: position, velocity and time. However, GNSS signals can be highly degraded or denied in some challenging environments as urban canyons, forests, mines and indoor environments. In addition to these critical scenarios, no orientation (or attitude) information is provided by standard receivers. Thus, in order to enhance robustness and performance of navigation solutions, it is possible to rely on integration with GNSS of measurements coming from other sensors, such as inertial sensors, barometers, magnetometers, cameras and other systems that can be used for positioning. The objective is to go towards seamless positioning, integrating measurements adaptively based on availability and reliability of the different systems.

In the intelligent transportation systems, the automatic navigation system is the active research domain. The lane lines reconstruction based on the computer vision is the kernel technique. This paper describes the multi-lane line reconstruction with a single view for highway application. We calibrate the camera and get the model of the road. Then, we detect the white lane lines and reconstruct the multi lane lines. This paper also analysis the basic way in keeping the road based on the vision model and the lane line model. We have experimented the system with the algorithms on highway at 150 km/h in Sichuan province and Chongqing city in China. The result shows that the algorithms can work perfectly.

In intelligent driving, lane line detection is a basic but challenging task, especially in complex road conditions. The current detection algorithms based on convolutional neural networks perform well for simple scenes with plenty of light, and the lane lines are clean and unobstructed. Still, they do not perform well for complex scenes such as damaged, blocked, and lack-of-light scenes. In this article, we have exceeded the above restrictions and propose an attractive network: LaneFormer; We use an end-to-end network for up and down sampling three times each, then fuse them in their respective channels to extract the slender lane line structure. At the same time, a correction module is designed to adjust the dimensions of the extracted features using MLP, judging whether the feature is completely extracted through the loss function. Finally, we send the feature into the transformer network, detect the lane line points through the attention mechanism, and design a road and camera model to fit the identified lane line feature points. Our proposed method has been validated in the TuSimple benchmark test, showing the most advanced accuracy with the lightest model and fastest speed.

Safe and autonomous driving is one of the most important challenges in mobile robotics and has been received considerable attention over the past years in indoor and outdoor navigations. Most methods developed so far immediately activate an obstacle avoidance algorithm when a robot meets obstacles without predicting the motion of the obstacle. These methods would be inefficient for the navigation in urban environments with traffic lane because the traffic lane becomes a constraint in the robot motion. For the safe driving in urban environments, it is efficient to consider this constraint before performing an obstacle avoidance algorithm in the planning phase when the robot meets an obstacle. Therefore, a decision making algorithm for safe driving in case of navigating on the road is needed. In terms of its simplicity and its short response time, a fuzzy algorithm is especially suitable for real-time applications. In this paper, we propose a fuzzy-based decision making algorithm for the outdoor navigation of mobile robots. The algorithm is tested in crossroad environment. To satisfy the robot's safety requirements and to spend less time on the intersection, we designed our navigation algorithm consists of two primary parts: perception (understanding environment) and decision making part. This paper focuses on the decision making part. Simulation results show the algorithm's effectiveness.