A fuzzy decision making algorithm for safe driving in urban environment

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%.

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.

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.

Lane detection is an essential visual capability for the autonomous vehicle navigation used in the intelligent transportation system (ITS). Several approaches for lane detection have been suggested in the past. However, there is still one issue about robustness. This paper presents a robust and real-time multi-lane detection method based on road marking feature points (RMFP) for autonomous vehicle navigation in an urban environment. The key idea in this paper is to apply methods from extracting RMFP and the target tracking domain to identify lane information. Then the RMFP is extracted from the gray-scale image and the IPM image. Additionally, the lane line color and structure features are used to sift through the RMFP that meet lane lines. Lastly, the clustering method is used to generate lane lines, and these lines are tracked by frame association and Kalman Filter. The experiment results based on image sequences taken under various visual conditions showed that the proposed system can identify the lane line markings with 98% accuracy. The method shows much robustness under various complicated scenarios and meets the real-time requirement.

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.

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.

Mitigating multipath of cellular long-term evolution (LTE) signals for robust positioning in urban environments is considered. A computationally efficient receiver, which uses a phase-locked loop (PLL)–aided delay-locked loop (DLL) to track the received LTE signals, is presented. The PLL-aided DLL uses orthogonal frequency division multiplexing (OFDM)–based discriminator functions to estimate and track the time-of-arrival. The code phase and carrier phase performances in an additive white Gaussian noise (AWGN) channel are evaluated numerically. The effects of multipath on the code phase and carrier phase are analyzed, demonstrating robust multipath mitigation for high transmission LTE bandwidths. The average of the DLL discriminator functions over multiple LTE symbols is presented to reduce the pseudorange error. The proposed receiver is evaluated on a ground vehicle in an urban environment. Experimental results show a root mean square error (RMSE) of 3.17 m, a standard deviation of 1.06 m, and a maximum error of 6.58 m between the proposed LTE receiver and the GPS navigation solution over a 1.44 km trajectory. The accuracy of the obtained pseudoranges with the proposed receiver is compared against two algorithms: estimation of signal parameters by rotational invariance techniques (ESPRIT) and EKAT (ESPRIT and Kalman filter).

Three-dimensional navigation will be an essential component of low-altitude unmanned rotorcraft operations in urban environments. Successful navigation will require that the vehicle sense the surrounding obstacles, incorporate the data into its world model, and react to new obstacles to ensure both vehicle survivability and satisfactory completion of the mission objectives. A complete navigation solution built on heuristic planning concepts is presented. A fast A*-based 3D route planner is compared with one that constructs 3D routes by executing a 2D planner on plane slices of the terrain. Monte Carlo simulation evaluation and flight test validation results are presented.

The ATENEA (Advanced Techniques for Navigation Receivers and Applications) project aims to join deeply integrated GNSS/INS receiver architectures and LIDAR techniques to provide an advanced navigation solution. The approach is suitable for a wide range of surveying applications in difficult environments, being Urban Mapping selected as reference case. This paper presents the integrated GNSS+INS+LiDAR navigation filter, and detailed results of the test campaign carried out using both synthetic and real data. These results validate the ATENEA concept and open the possibilities of using such system for reliable navigation in urban environments.

Geographical information systems for map based navigation in urban environments

Many applications demand high-precision navigation in urban environments. Two frequency real-time kinematic (RTK) Global Positioning System (GPS) receivers are too expensive for low-cost or consumer-grade projects. As single-frequency GPS receivers are getting less expensive and more capable, more people are utilizing single-frequency RTK GPS techniques to achieve high accuracy in such applications. However, compared with dual-frequency receivers, it is much more difficult to resolve the integer ambiguity vector using single-frequency phase measurements and therefore more difficult to achieve reliable high-precision navigation. This paper presents a real-time sliding-window estimator that tightly integrates differential GPS and an inertial measurement unit to achieve reliable high-precision navigation performance in GPS-challenged urban environments using low-cost single-frequency GPS receivers. Moreover, this paper proposes a novel method to utilize the phase measurements, without resolving the integer ambiguity vector. Experimental results demonstrate real-time position estimation performance at the decimeter level. Furthermore, the novel use of phase measurements improves the robustness of the estimator to pseudorange multipath error.

<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>

<div class="section abstract"> <div class="htmlview paragraph">This paper investigates the use of artificial intelligence (AI) to enhance autonomous vehicle (AV) controls in unstructured off-road environments. Recent research developments address the utilization of AI and deep learning (DL) models to share information with AVs, enabling intelligent navigation in urban environments. The suggested research allows AVs to navigate in an off-road environment by utilizing AI-predicted trajectories while avoiding obstacles. Our method aims to enhance vehicle autonomy, enabling it to move promptly and responsively while interacting with other vehicles in an off-road environment. We demonstrate the efficacy of AI-driven trajectory predictions through comprehensive simulation validations. Moreover, the research demonstrates the use of AI prediction to construct a proactive Model Predictive Controller (MPC) for off-road autonomous vehicles. Simulation studies and comparisons with existing approaches validate the effectiveness and advantages of the proposed approach for off-road autonomous vehicle controls.</div> </div>

Pedestrian navigation in an urban environment based on traditional digital mapping systems is constrained by the inherent limitations of existing online mapping services. The major challenges include the reliance on global positioning system (GPS), whose signals are not received well in some locations, and the inferior user experience caused by the lack of information about unknown surroundings. This article describes the design and development of a markerless augmented reality-based pedestrian navigation system that can handle navigation even in the absence of GPS signals. It improves the user experience by providing a novel landmark recognition feature, which allows users to identify nearby buildings or streets during navigation. To mitigate the absence of GPS signals, a novel user localization method utilizing a step count-based distance estimator is proposed. The experimental results show the location accuracy of 2.3 meters on average and the increase in step count detection accuracy of nearly 0.5% in comparison with existing technologies available in smartwatches, and an average latency of 74 milliseconds in system response in an urban environment. The proposed solution can be used as a mobile application on smartphones and has the potential to contribute to the smart city-related standardization activities of ITU-T Study Group 16.

Our modern society has long known the scourge of road accidents. This phenomenon leads to serious material and human losses. Building systems to reduce or eliminate these accidents, especially those relating to pedestrians, has become a priority for cities. The objective of this paper is to develop a real-time distributed solution for the safe navigation assistance of pedestrians. We provide fuzzy indicators of pedestrians’ risk exposure to road accidents which we integrate into a safest fuzzy path finding service. This work also concerns the development of real-time distributed pipeline architecture to collect the positions of pedestrians and vehicles in real time using a GPS tracker. The data collected will be visualized and stored for analysis and exploration purposes. To perform distributed processing in real time, we use a real-time architecture orchestrated by reactive technologies based on containerized micro-services.KeywordsFuzzy riskPedestrian’s safetyDistributed architectureFuzzy path