Deep Learning-Based Perception and Control Systems for Autonomous Vehicles

The rapid advancement of deep learning has significantly transformed the development of next-generation autonomous vehicles, enhancing both safety and efficiency. This paper explores the integration of deep learning techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning, in perception, decision-making, and control systems of autonomous vehicles. By leveraging vast datasets and real-time processing, deep learning enables precise object detection, path planning, and adaptive driving strategies. Furthermore, the implementation of sensor fusion techniques combining LiDAR, radar, and cameras enhances situational awareness, reducing the risk of accidents. Despite these advancements, challenges such as computational complexity, adversarial robustness, and ethical considerations remain key research areas. This study provides an overview of the current state-of-the-art deep learning applications in autonomous vehicles and discusses future directions toward fully autonomous, safer, and more efficient transportation systems.

Nowadays, the development of modern technologies in the transportation area has increased rapidly. The latest technology is focused on autonomous vehicles. The most important aspect in autonomous vehicle control systems is safety, smooth operation and comfort. It can be provided by the natural element which is from human behavior as a reference to the autonomous control system. This paper is briefly describing the human behavior during braking for autonomous electric vehicle (AEV) control system development. To obtain the human behaviour during accelerating and braking, two units of angle sensors were installed to the accelerator and brake pedal of an electric car to measure the angle of both pedals. Real-time speed is recorded by using a GPS unit. The experiment is focused on braking characteristic of the electric vehicle. It was carried out based on four different distances with fixed initial speed before braking action was recorded. A software interface was designed to display and control real-time speed and pedal angle value during operation.

The recent development of autonomous vehicles has attracted much attention, but operating these vehicles may be too complex for average users. Therefore, we propose an intuitive, multimodal interface for the control of autonomous vehicles using speech and gesture recognition to interpret and execute the commands of users. For example, if the user says “turn there” while pointing at a landmark, the vehicle can utilize this behavior to correctly understand and comply with the user’s intent. To achieve this, we designed a two-part interface consisting of a multimodal understanding component and a dialog control component. Our multimodal understanding and dialog control components can be seen as a concatenation of two separate transducers. One transducer is used for multimodal understanding and the other for a conventional dialog system. We then construct a combined transducer from these two transducers. We developed various scenarios which might arise while operating an autonomous vehicle and displayed these scenes on a monitor. Subjects were then asked to operate a virtual car using speech commands and pointing gestures to control the vehicle while observing the monitor. The questionnaire results show that subjects felt they were able to easily and naturally operate the autonomous vehicle using utterances and gestures.

Greater prosociality toward other human drivers than autonomous vehicles: Human drivers’ discriminatory behavior in mixed traffic

This article presents an in‐depth review of path following control strategies that are applicable to a wide range class of marine, ground, and aerial autonomous robotic vehicles. From a control system standpoint, path following can be formulated as the problem of stabilizing a path following error system that describes the dynamics of position and possibly orientation errors of a vehicle with respect to a path, with the errors defined in an appropriate reference frame. In spite of the large variety of path following methods described in the literature we show that, in principle, most of them can be categorized in two groups: stabilization of the path following error system expressed either in the vehicle's body frame or in a frame attached to a “reference point” moving along the path, such as a Frenet‐Serret (F‐S) frame or a Parallel Transport (P‐T) frame. With this observation, we provide a unified formulation that is simple but general enough to cover many methods available in the literature. We then discuss the advantages and disadvantages of each method, comparing them from the design and implementation standpoint. We further show experimental results of the path following methods obtained from field trials testing with under‐actuated and over‐actuated autonomous marine vehicles. In addition, we introduce open‐source Matlab and Gazebo/ROS simulation toolboxes that are helpful in testing path following methods before their integration in the combined guidance, navigation, and control systems of autonomous vehicles.

The attitudes of the public play a critical role in the acceptance, purchase, utilization, and research and development of autonomous vehicles (AVs). Currently, the attitudes of the public toward AVs have been mostly estimated through traditional survey data, which bears a low quantity of samples with high labor costs. It is probably also one of the reasons why the critical factors on the attitudes of the public toward AVs have not been studied from a comprehensive perspective yet. To address the issue, this study aims to propose a method by using large-scale social media data to investigate key factors that affect the attitudes of the public toward AVs. A total of 954,151 Twitter data related to AVs and 53 candidate independent variables from seven categories were extracted using the web scraping method. Then, sentiment analysis was used to measure the public attitudes towards AVs by calculating sentiment scores. Random forests algorithm was employed to preliminarily select candidate independent variables according to their importance and a linear mixed model was utilized to explore the impacting factors, considering the unobserved heterogeneities caused by the subjectivity level of tweets. The results showed that the attitudes of the public toward AVs were slightly optimistic. Factors, such as “drunk”, “blind spot”, and “mobility”, had the largest impacts on public attitudes. In addition, people were more likely to express positive feelings when talking about words, such as “lidar” and “Tesla”, related to high technologies. Conversely, factors, such as “COVID-19”, “pedestrian”, “sleepy”, and “highway”, were found to have significantly negative effects on the attitudes of the public. The findings of this study are beneficial for the development of AV technologies, the guidelines for AV-related policy formulation, and the understanding and acceptance of the public toward AVs.

This article describes overview of automated and, in perspective, autonomous vehicles’ (AV) implementation risks. Set of activities, actual before the use of AVs on public roads, minimizing negative technical and social problems of AVs’ implementation is presented. Classification of vehicle’s automated control systems operating conditions is formulated. Groups of tests for AVs are developed and justified, sequence of AVs’ testing system formation is proposed.

Intelligent ground vehicles require automated steering control systems. The complexity of the automated steering systems has increased as the concurrent data receiving, processing, decision-making and monitoring all happen simultaneously in super-fast sampling rates. The research reported in this paper focuses on the neural network training and machine learning approach for an automated steering system. The essential information of the steering controller was trained using artificial neural network (ANN) and pattern recognition algorithm approaches. The objective is to investigate the design of ANN training and deep machine learning on collected data from sensors of steering wheel angular position, speed, steering column torque, and ego vehicle speed and generating acceptable steering commands for the vehicle. The results of the research showed that the proposed network control system had trained and validated more than 96.5% steering system behavior patterns and adapted large random disturbances of the steering controller commands. It is, therefore, necessary to develop artificial intelligence methodologies in automated steering systems of autonomous vehicles with neural network representing the main topology blocks of the control system architecture and utilize ANN abstraction in the control system of autonomous vehicle steering control system.

Perception of autonomous vehicles – A Ghanaian perspective

Recently, the development of autonomous vehicles (AVs) and intelligent driver assistance systems has drawn significant attention from the public. Despite these advancements, AVs may encounter critical situations in real-world scenarios that can lead to severe traffic accidents. This review paper investigated these critical scenarios, categorizing them under weather conditions, environmental factors, and infrastructure challenges. Factors such as attenuation and scattering severely influence the performance of sensors and AVs, which can be affected by rain, snow, fog, and sandstorms. GPS and sensor signals can be disturbed in urban canyons and forested regions, which pose vehicle localization and navigation problems. Both roadway infrastructure issues, like inadequate signage and poor road conditions, are major challenges to AV sensors and navigation systems. This paper presented a survey of existing technologies and methods that can be used to overcome these challenges, evaluating their effectiveness, and reviewing current research to improve AVs’ robustness and dependability under such critical situations. This systematic review compares the current state of sensor technologies, fusion techniques, and adaptive algorithms to highlight advances and identify continuing challenges for the field. The method involved categorizing sensor robustness, infrastructure adaptation, and algorithmic improvement progress. The results show promise for advancements in dynamic infrastructure and V2I systems but pose challenges to overcoming sensor failures in extreme weather and on non-maintained roads. Such results highlight the need for interdisciplinary collaboration and real-world validation. Moreover, the review presents future research lines to improve how AVs overcome environmental and infrastructural adversities. This review concludes with actionable recommendations for upgrading physical and digital infrastructures, adaptive sensors, and algorithmic upgrades. Such research is important for AV technology to remain in the zone of advancement and stability.

In this paper a novel method is presented for hierarchical control of autonomous vehicles consisting of high-level (HLC) and low-level (LLC) control systems. The LLC system can be commanded to assure a prescribed velocity and steering profile using PID-type active suspension, velocity and steering control. The outputs of the LLC system are the generalized forces and moments required by the vehicle. Because of its nature, the LLC system cannot eliminate position and orientation errors caused by the unknown centre of gravity point, drifting and time-varying friction. These errors are compensated by the HLC which uses the kinematic model of the vehicle to generate the reference trajectory from the prescribed profiles and employs a linear quadratic optimal controller to compensate the tracking errors. The control algorithm minimizes the perturbations and the optimal solution is realized as a time-varying state-feedback control law. The efficiency of the hierarchical vehicle control system is validated for realistic motion maneuvers by simulation on the nonlinear vehicle model using multibody robotic formalism.

Transportation sustainability is adversely affected by recurring traffic congestions, especially at urban intersections. Frequent vehicle deceleration and acceleration caused by stop-and-go behaviours at intersections due to congestion adversely impacts energy consumption and ambient air quality. Availability of the maturing vehicle technologies such as autonomous vehicles and Vehicle-To-Vehicle (V2V) / Vehicle-To-Infrastructure (V2I) communications provides technical feasibility to develop solutions that can reduce vehicle stops at intersections, hence enhance the sustainability of intersections. This paper presents a next-generation intersection control system for autonomous vehicles, which is named ACUTA. ACUTA employs an enhanced reservation-based control algorithm that controls autonomous vehicles’ passing sequence at an intersection. Particularly, the intersection is divided into n-by-n tiles. An intersection controller reserves certain time-space for each vehicle, and assures no conflict exists between reservations. The algorithm was modelled in microscopic traffic simulation platform VISSIM. ACUTA algorithm modelling as well as enhancement strategies to minimize vehicle intersection stops and eventually emission and energy consumption were discussed in the paper. Sustainability benefits offered by this next-generation intersection were evaluated and compared with traditional intersection control strategies. The evaluation reveals that multi-tile ACUTA reduces carbon monoxide (CO) and Particulate Matter (PM) 2.5 emissions by about 5% under low to moderate volume conditions and by about 3% under high volume condition. Meanwhile, energy consumption is reduced by about 4% under low to moderate volume conditions and by about 12% under high volume condition. Compared with four-way stop control, single-tile ACUTA reduces CO and PM 2.5 emissions as well as energy consumption by about 15% under any prevailing volume conditions. These findings validated the sustainability benefits of employing next-generation vehicle technologies in intersection traffic control. In addition, extending the ACUTA to corridor level was explored in the paper.

This paper designed a control system of small autonomous underwater vehicle(AUV). A distributed control architecture that is MOOS-IvP is applied in this control system. Using this control architecture, there are distribute and modular characteristic in this control system. It comprises of the sensor module, the propulsion module, the fault detection module, the data log module, the device initialize module, the navigation module, the control module and the intelligent decision module. This control system can realize the constant depth control, the constant heading control, the constant speed control and the waypoint behavior control. We finished these control experiments and verified this control system by the lake experiment in June 2015.

The development of autonomous underwater vehicles (AUVs) for scientific, military and commercial purposes in applications such as ocean surveying, unexploded ordnance hunting and cable tracking and inspection requires the correspond ing development of navigation, guidance and control (NGC) systems, which should work in accord with each other for proper operation. Navigation systems are necessary to provide knowledge of vehicle position and attitude. The guidance systems manipulate the output of the navigation systems to generate suitable trajectories to be followed by the vehicle. This takes into account the target and any obstacles that may have been encountered during the course of a mission. The control systems are responsible for keeping the vehicle on course as specified by the guidance processor. In the Hammerhead AUV, this is achieved through manipulating the rudder and the hydroplanes (canards) of the vehicle. The need for accuracy in NGC systems is paramount. Erroneous position and attitude data in navigation systems can lead to a meaningless interpretation of the collected data, which in turn affects the accuracy of the corresponding guidance and control systems. This, if not contained properly may lead to a catastrophic failure of an AUV during a specific mission. The integrated NGC of the Hammerhead AUV is depicted.

An autonomous vehicle is designed to be driven automatically without a crew controlling it. The autonomous vehicle can ease the people in driving. Thus, a control system is important to ensure the autonomous electric vehicle run properly. Various control systems can be implemented on the autonomous vehicle. However, the previous studies have shown that the inaccuracy and inconsistency may still occur. Thus, this study presents the combination of compass and fuzzy logic to control the position of the autonomous vehicle. The Sugeno fuzzy logic method is used to control the position of the movement of autonomous vehicle by considering several inputs, such as the direction to the destination, the position of the destination, and the distance to the intended target. Meanwhile, the rotational speed of the motor becomes the output. In this study, the autonomous vehicle was run in Universitas Sriwijaya located in two locations, namely at Palembang Campus, and Indralaya Campus. The results show that the autonomous vehicle was able to reach the destination position with the motor speed output on the autonomous electric vehicle which was read according to the fuzzy. This study shows that fuzzy rule for 5 memberships function has the lowest heading error of 6.8 degrees compared to 3 memberships function. Nevertheless, 3 memberships function has the shortest time to reach the destination. The results of this study indicate that the control system using the fuzzy logic method works well.