When it comes to some essential abilities of autonomous ground vehicles (AGV), detection is one of them. In order to safely navigate through any known or unknown environment, AGV must be able to detect important elements on the path. Detection is applicable both on-road and off-road, but they are much different in each environment. The key elements of any environment that AGV must identify are the drivable pathway and whether there are any obstacles around it. Many works have been published focusing on different detection components in various ways. In this paper, a survey of the most recent advancements in AGV detection methods that are intended specifically for the off-road environment has been presented. For this, we divided the literature into three major groups: drivable ground and positive and negative obstacles. Each detection portion has been further divided into multiple categories based on the technology used, for example, single sensor-based, multiple sensor-based, and how the data has been analyzed. Furthermore, it has added critical findings in detection technology, challenges associated with detection and off-road environment, and possible future directions. Authors believe this work will help the reader in finding literature who are doing similar works.

SPIE Digital Library Proceedings

The transportation system is moving towards higher levels of autonomy. Longitudinal speed control, or adaptive cruise control (ACC), is intended to provide driver assistance by controlling vehicle speed and maintaining a safe distance from the preceding vehicle. Artificial intelligence (AI) and machine learning (ML) paved the way for robust navigation and decision-making in complex environments. In this paper, we propose a comprehensive framework for speed control using a deep reinforcement learning algorithm for safety and comfort. We incorporate the deep deterministic policy gradient (DDPG) framework with an attention mechanism for this. Many works achieved smooth vehicle control output from the DDPG algorithm with different network structures, but we demonstrate that adding an attention mechanism improves the overall performance. The baseline DDPG framework is based on fully connected layers. However, when we bring the attention mechanism to the DDPG model, it helps to increase focus on the most important features and enhances the overall model effectiveness. We also designed a custom reward function as our priority is to improve overall safety and comfort. The efficacy of our model is evaluated in comparison to the baseline DDPG model, emphasizing the influence of the attention mechanism. We have performed an ablation study to determine the impact of the number of layers and neurons in the hidden layer. In addition, we tested this model on diverse datasets (simulated and real), including some unknown scenarios in the testing datasets. We demonstrate that our architecture has outperformed the state-of-the-art model, and is quite robust across different datasets.

Navigating through an unknown environment is one of the key capabilities of the autonomous ground vehicle (AGV). It is relatively easier to traverse through an on-road environment, but moving through an off-road environment is challenging because of the inadequate driving path, obstacles, surface roughness, slope, dense vegetation, poor soil conditions, lack of road signs, etc. This complexity requires simulation of the AGV through many environments before facing an unknown situation in the real world. This paper proposes a dynamic path planning technique for AGV navigation in an off-road environment. First, a brief discussion on the traversability model and the factors mentioned in state of art such as vegetation density, soil condition, surface roughness, and slope individually. Secondly, we have proposed some modifications in the traversability model by introducing weights and exponents with each factor. Then, the A* algorithm has been analyzed by penalizing the weight and exponent values to get an optimal path. We used the Mississippi State University Autonomous Vehicular Simulator (MAVS) for simulation by creating an off-road scenario and using an AGV. We have generated a cost map based on the traversability score. The higher the score, the better the result. The optimal path is selected considering the traversability score. The novelty of this work is that we are exploring the linearity and non-linearity of the traversability model and applying the A* algorithm for path planning.

Mobile devices have distinct RF fingerprints, which are reflected by changes in the frequency of transmitted signals. The Short-Time Fourier Transform (STFT) is a suitable technique for evaluating this frequency content and thus identifying them. In this paper, we take advantage of STFT processing and perform roomlevel location classification. The raw in-phase and quadrature (IQ) signals and channel state information (CSI) frames have been collected using seven different cell phones. The data collection process has been performed in eight different locations on the same floor of our engineering building, which contains indoor hallways and rooms of different sizes. Three software-defined radios (SDRs) are placed in three different locations to receive signals simultaneously but separately. The IQ and CSI frames have been concatenated together for training a neural network. A Multi-Layer Perception (MLP) network has been used to train the concatenated signals as input and their corresponding locations as labels. A challenging aspect is that our dataset does not contain the same number of samples per location. Moreover, several locations have insufficient training data due to signal attenuation. An imbalanced learning method has been implemented in this dataset to overcome this limitation and improve the classification accuracy. The classification strategy involves binary classification like individual location vs. other. Using this approach, we obtain a mean accuracy of around 95%.

Artificial intelligence (AI) in autonomous vehicles (AVs) is gaining much focus on safety, comfort, and efficiency. The goal is to provide the driver with assistance and mimic human driving. Deep reinforcement learning (DRL), specifically the deep deterministic policy gradient (DDPG) is often used for longitudinal velocity control, utilizing the velocities of the vehicles and the distance between them. DDPG has been found effective in many studies when it is embedded with a self-supervised learning (SSL) method. In this paper, a framework for longitudinal velocity control is proposed using SSL and DDPG frameworks. The inputs of the DDPG networks have been replaced by the outputs of the SSL network. The primary objective of this SSL is to enable the model to accurately predict future states based on current states and actions. The input features of the datasets have been modified so that the DDPG model can get additional information that helps the model make better predictions. These features include the distance between vehicles, ego vehicle velocity, acceleration, jerk, and lead vehicle relative velocity, and estimated velocity. Furthermore, a custom reward function is designed to account for safety, driving comfort, negative impact, driving aggressiveness, and fuel efficiency. In order to evaluate the model, the algorithm has been trained and tested on a variety of datasets, including simulated and real-world data. The analysis demonstrates that the new architecture maintains strong robustness across various datasets and outperforms the current state-of-the-art models.