Comparative study of different controllers in an autonomous vehicle system

For the past few years, the automation of transportation becomes a hot research topic for smart cities. Intelligent Transportation System (ITS) aims to manage and optimize the traffic congestion, road accidents, parking allocation using Autonomous Vehicles (AV) system, where the AVs are internally connected for message passing and critical decision making in time-sensitive applications. The data security in such applications can be offered using Blockchain (BC) technology. But, as per the existing literature, there exists no system which can call AVs automatically based on the situation, i.e., call an ambulance in case of an accident, call logistic service in case of home transfer, and call the traffic department in case of traffic jam. Motivated from the aforementioned reasons, in this article, we propose a BC-based secure and intelligent sensing and tracking architecture for AV system using beyond 5G communication network. Recently, AVs are facing issues with sensing and tracking technology as well as the data thefts. AV system contains sensitive information and transfers it through a communication channel to Connected AVs (CAVs), where the corrupted information or delay of a fraction of a second can lead to a critical situation. So, here we present possib the attacks and safety countermeasures using BC technology to protect the AV system. The proposed architecture ensures secure sensing and tracking of an object through BC by deploying AI algorithms at the edge servers. Also, the beyond 5G network enables communications with low latency and high reliability to meet the desires of the aforementioned time-sensitive applications. The proposed system is evaluated by considering the parameters as mobility and data transfer time against the traditional LTE-A and 5G communication networks. The proposed system outperforms traditional systems and can be suitable for diverse applications where latency, reliability, and security are the prime concerns.

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

The performance of autonomous vehicle systems is currently limited by their inability to accurately analyze their surrounding environment. In order to function in a dynamic real world environment, an autonomous vehicle system must be capable of interpreting terrain based upon predetermined mission goals. This paper describes a concept of knowledge-based terrain analysis currently being developed to support the information needs of an autonomous helicopter system. The terrain analysis system consists of five integrated processing stages. Each process is discussed in detail and supported by a number of mission oriented examples. Int roduction One of the most unique system concepts being explored today is that of the development of totally autonomous land, sea, and air vehicles [1 - 3]. For military applications, autonomous vehicles provide a mechanism for removing humans from modern day battlefields while not impacting the tactical capabilities of the battle force. Civilian applications for such a device entail the investigation of hazardous areas (e.g. nuclear reactors and toxic waste dumps), search over large areas (e.g. downed aircraft or lost campers) and monitoring of large facilities (e.g. robot security police). As military funding for these systems is currently at an alltime high, a variety of autonomous system concepts are continually being presented in the technical literature. The primary limitation of an autonomous vehicle still lies in its inability to accurately analyze surrounding terrain information in order to generate tactical plans of action. This paper describes activities being performed in the Artificial Intelligence Branch of the Georgia Tech Research Institute on knowledge-based terrain analysis, particularly in support of the autonomous helicopter program.System OverviewThe knowledge-based tactical terrain analysis system consists of five separate processing phases: [1] image segmentation, [2] region classification, [3] visual model, [4] route planning, [5] threat location and coverage. Each processing phase is briefly discussed in the following subsections with actual image results given where appropriate.

Transitioning to a driverless city: Evaluating a hybrid system for autonomous and non-autonomous vehicles

According to the world health organization, there were 1.25 million road accident deaths globally in 2013. To decrease this count, it has been said that autonomous vehicles can play a major role. The autonomous vehicle uses different sensors such as LIDAR, RADAR, Ultrasonic sensors, and cameras for detection of lane lines, objects, traffic sign, traffic lights, and driving path. There are several successful projects on autonomous vehicles, but the main limitations are high costs and regulations. The camera can be a cost-effective solution as compare to LIDAR in all the basic detection needed for autonomous driving. The proper placement of sensors on the vehicle plays an important role in efficient design, working and reducing the cost of the overall autonomous vehicle system. This paper discusses the parameters considered while deciding the positioning of the camera, and the geometric calculation for the best positioning of a monocular camera on an autonomous vehicle.

Liability Design for Autonomous Vehicles and Human-Driven Vehicles: A Hierarchical Game-Theoretic Approach

Technological development and the massive investment in 'intelligent' vehicle technologies are likely to turn autonomous vehicles into reality in a few years. The insertion intelligence in road vehicles aims to obtain a reduction in traffic accidents due to the mitigation of errors committed by human drivers, thanks to their replacement by machines. In addition, autonomous vehicles should be able to mitigate hazards in road transportation systems without creating new risks. Thus, It is important to study how to ensure safety in this new scenario. Some research in this area has already been developed, but they do not show how to design properly an autonomous vehicle system in which existing methods can be applied to analyze and guarantee adequate levels of safety in such vehicles. As a result, this master's work develops a proposal that aims to facilitate the development and analysis of this new class of vehicles, in addition to ensuring levels of critical safety appropriate to autonomous vehicles. The proposal is represented by a system called Autonomous Vehicle Control (CVA), which was developed under the concept of Intelligent Transport Systems (STI). The CVA system is formed by two layers, one of operation (Autonomous Vehicle Operation -OVA), responsible for the driving of the vehicle and another one of protection (Autonomous Vehicle Protection -PVA). The main idea is to use the PVA layer for the prevention of accidents. The PVA layer was developed and tested in a simulation environment, considering a Case Study. It was observed that, as predicted, the CVA system, because it has a layer aimed at vehicular protection, was able to avoid several collision situations between vehicles.

The rapid advancement of autonomous vehicle systems (AVS) has introduced complex challenges to road safety. While some studies have investigated the contribution of factors influencing AV-involved crashes, few have focused on the impact of vehicle-specific factors within AVS on crash outcomes, a focus that gains importance due to the absence of a human driver. To address this gap, the advanced machine learning algorithm, LightGBM (v4.4.0), is employed to quantify the potential effects of vehicle factors on crash severity and collision types based on the Autonomous Vehicle Operation Incident Dataset (AVOID). The joint effects of different vehicle factors and the interactive effects of vehicle factors and environmental factors are studied. Compared with other frequently utilized machine learning techniques, LightGBM demonstrates superior performance. Furthermore, the SHapley Additive exPlanation (SHAP) approach is employed to interpret the results of LightGBM. The analysis of crash severity revealed the importance of investigating the vehicle characteristics of AVs. Operator type is the most predictive factor. For road types, highways and streets show a positive association with the model’s prediction of serious crashes. Crashes involving vulnerable road users can be attributed to different factors. The road type is the most significant factor, followed by precrash speed and mileage. This study identifies key predictive associations for the development of safer AV systems and provides data-driven insights to support regulatory strategies for autonomous driving technologies.

Rapid advancements in vehicle automation technology have enabled autonomous vehicles (AVs) to move beyond simple, structured highway settings and operate increasingly within more complex urban environments. As AVs become more prevalent, they will inevitably share the road with diverse traffic participants, particularly pedestrians. The high uncertainty and dynamic nature inherent in human behavior significantly complicate the AV’s decision-making process. To ensure both safety and efficiency while exhibiting socially acceptable behavior, AVs must make real-time decisions and continuously adapt their strategies in response to surrounding pedestrian behaviors. This poses a major challenge to existing AV decision-making systems. Therefore, this study focuses on developing the decision-making strategies for AVs in urban pedestrian-involved environments, spanning scenarios from simple interactions with a single pedestrian to complex cases involving multiple pedestrians. The core objective is to uncover the underlying interaction dynamics and to improve the AV’s capability to make decisions that are safe, efficient and socially acceptable in dynamic, human-centric contexts. Initially, this study investigates the critical factors influencing the decision making processes of human drivers and pedestrians during vehicle–pedestrian interactions. A series of controlled experiments were conducted using a virtual reality platform designed to collect realistic behavioral data. The data analysis mainly focused on kinematic variables that are easily measurable in real-world deployments. AdaBoost classification and Partial Dependence Plots were employed to identify and visualize the most influential factors affecting pedestrian crossing intentions and driver approaching behaviors. The results indicate that longitudinal distance and vehicle acceleration are the most influential factors in pedestrian decision-making, while pedestrian speed and longitudinal distance also play a crucial role in determining whether the vehicle yields or not. Based on these insights, a simplified mathematical model was developed to relate observable kinematic parameters to pedestrian crossing intentions, providing a practical tool for dynamically inferring crossing intentions during interactions. Additionally, the study explored driver yielding patterns under varying degrees of pedestrian intention clarity. These findings support the development of interpretable and implementable models for real-time decision-making in vehicle–pedestrian interaction scenarios. In addition, this study examines the decision-making strategies of the AVs when interacting with a single pedestrian in unsignalized intersections. A novel framework was proposed that integrates the Partially Observable Markov Decision Process with behavioral game theory to dynamically model interactive behaviors between the AVs and the pedestrian. Both agents were modeled as dynamic-belief-induced quantal cognitive hierarchy models, considering human reasoning limitations and bounded rationality in the decision-making process. Moreover, a dynamic belief updating mechanism allowed the AV to update its understanding of the opponent’s rationality degree in real-time based on observed behaviors and adapt its strategies accordingly. The analysis results indicate that proposed models effectively simulate vehicle-pedestrian interactions and the AV decision-making approach performs well in safety, efficiency, and smoothness. It captures key patterns of the driving behavior operated by real human drivers in virtual reality experiments and even achieves more comfortable navigation compared to our previous virtual reality experimental data. Finally, this study investigates the decision-making strategies of the AVs operating in pedestrian-rich shared spaces. A novel framework was proposed for modeling interactions between the AV and multiple pedestrians. In this framework, a cognitive process modeling approach inspired by the Free Energy Principle was integrated into both the AV and pedestrian models to simulate more realistic interaction dynamics. Specifically, the proposed pedestrian Cognitive-Risk Social Force Model adjusts goal-directed and repulsive forces using a fused measure of cognitive uncertainty and physical risk to produce human-like trajectories. Meanwhile, the AV leverages this fused risk to construct a dynamic, riskaware adjacency matrix for a Graph Convolutional Network within a Soft Actor-Critic architecture, allowing it to make more reasonable and informed decisions. The qualitative and quantitative results indicate that the proposed strategy effectively improves safety, efficiency, and smoothness of AV navigation compared to the state-of-the-art method. In conclusion, this study addresses key challenges in AV decision-making within human-centric urban environments, providing valuable insights as well as practical solutions to support safe, efficient, and socially aware autonomous navigation.

This article examines the transformative impact of Large Language Models (LLMs) on autonomous vehicle technology, analyzing how these advanced AI systems are reshaping the fundamental architecture of self-driving systems. Moving beyond traditional modular pipelines, LLM-powered autonomous vehicles demonstrate enhanced contextual awareness, flexible decision-making, and intuitive human-machine interaction capabilities previously unattainable with conventional approaches. The integration of language model capabilities enables vehicles to process multimodal data streams cohesively, reason about complex driving scenarios, and communicate more effectively with passengers and other road users. Through case studies on industry implementations like Waymo's EMMA and research innovations such as DriveMLM, we identify key methodological advances, performance improvements, and remaining challenges in computational requirements, safety validation, and regulatory compliance. The article highlights promising research directions including hybrid AI architectures, edge computing optimization, and human-centric interaction models that will likely shape the future development of autonomous transportation systems. This convergence of language understanding and physical navigation represents a paradigm shift that promises to accelerate progress toward more capable, adaptable, and socially-aware autonomous vehicles.

Various papers on cyberwarfare in virtual environments and cybersecurity in intelligent systems have been published. Work has focused on the integration of cyberwarfare communication effects into a live–virtual–constructive (LVC) environment in order to better represent a network centric battlespace subject to cyber-attack, at a large force level. In addition, virtual cyber ranges have been developed. A virtual cyber range is a portable modeling and simulation framework that provides a real-time, hardware-in-the-loop capability for simulation of cyber threats to the entire net-centric infrastructure. The framework enables interoperability with LVC simulations, providing training and assessment of human-in-the-loop performance. This work builds on previous work and shows the need for a framework to support the modeling and simulation of cyber-attack and defense for training and assessment. This work does focus on cybersecurity for wireless communications, but the focus is on large network-centric systems at force level. Furthermore, there are published descriptions of research focused on the use of multi-agent systems in vehicle-to-vehicle communication and autonomous driving systems, such as Google’s driverless car. While the vehicles are commercial by nature, there are many similarities to military autonomous vehicles (MAVs), in their system components and architecture as well as the cybersecurity threats that they face. While these works are related to the work presented in this paper, with their focus on synthetic environments and commercial autonomous systems, they are not closely related. The focus of this paper is on small point to point and point to multipoint wireless networks, comprised of only a few nodes, with the nodes representing MAVs and controllers. The US Army, Air Force, and Navy are continuing to increase their use of these systems, and are now focusing on cybersecurity. Due to the lagging focus on wireless networks and autonomous vehicles, there is not much published work that is related. Current unmanned systems were not built with cybersecurity considerations taken into account, and are thus vulnerable to cyber-attack. The objective of this paper is to present and describe the need for a cyber-attack and defense simulation framework to support the modeling and simulation for cybersecurity of autonomous vehicle systems used by US Armed Forces. These autonomous vehicle systems include unmanned aerial systems and unmanned ground systems. The paper describes a notional framework to support this type of modeling, as well as a detailed use case and example cyber-attack simulation system.

The automotive industry is developing trends in autonomous driving and connected vehicular systems. These vehicles can access and send the data, download the software updates, connect with other vehicles or other IoT devices via the internet or wireless communication. Autonomous vehicle control urges very strict requirements about the security of the communication channels used by the vehicle to exchange information and the control logic that performs complex driving tasks. So, the increased connectivity results in a heightened risk of a cyber-security attack. For maintaining the advances in safe communication, it is important to establish strong security for connected vehicular systems. For this, existing cybersecurity attacks must be considered to minimize future cybersecurity risks in the connected and autonomous vehicle systems. In this chapter, the authors will emphasize recent works on how autonomous vehicles can ensure strong operation under ongoing cyber security attacks and their possible solutions.

This study presents the path-following control design for a group of autonomous underwater vehicles in a plane with obstacle and collision avoidance. A distributed backstepping controller is exploited for an autonomous vehicle to design a path-following controller. Then, the controller is applied for the path-following of a group of autonomous vehicle systems in the formation setting. The graph theory is used to model the interconnection between the vehicles. To avoid obstacles and collisions, a dynamic limit-cycle-based algorithm is proposed. It is assumed that each obstacle is involved an elliptical shape and if the collision or conflict of one vehicle to the surrounded environmental obstacles is detected, the obstacle avoidance algorithm will be active to prevent collisions. Finally, the introduced algorithm is implemented and simulated for a group of underwater autonomous vehicles in MATLAB software. The simulation results illustrate the effectiveness and performance of the proposed distributed controller apparently.

Merging is, in general, a challenging task for both human drivers and autonomous vehicles, especially in dense traffic, because the merging vehicle typically needs to interact with other vehicles to identify or create a gap and safely merge into. In this paper, we consider the problem of autonomous vehicle control for forced merge scenarios. We propose a novel game-theoretic controller, called the Leader-Follower Game Controller (LFGC), in which the interactions between the autonomous ego vehicle and other vehicles with a priori uncertain driving intentions is modeled as a partially observable leader-follower game. The LFGC estimates the other vehicles’ intentions online based on observed trajectories, and then predicts their future trajectories and plans the ego vehicle’s own trajectory using Model Predictive Control (MPC) to simultaneously achieve probabilistically guaranteed safety and merging objectives. To verify the performance of LFGC, we test it in simulations and with the NGSIM data, where the LFGC demonstrates a high success rate of 97.5% in merging.

Numerous developments in technology toward autonomous vehicle systems (AVSs) have been performed for so many years all over the world. As our day‐to‐day life is becoming progressively dependent on automation vehicle system and control devices, the craze on automation advancements is expected to move closer through scientific technologies like artificial intelligence and robotics. From another point of view, the cyber threat to the AVS causes drastic accidents and traffic congestion by varying the speed differences among the vehicles. To overcome such shortcomings, this paper presented a convolutional neural network‐oppositional‐based Henry gas solubility optimization (CNN‐OHGS) algorithm for an autonomous vehicle control system to enhance the robustness of the vehicle. At the same time, the attackers attempt to embed the faulty or defective data into the sensor readings of the autonomous vehicle to interrupt the optimal distances among the automated vehicles. Therefore to minimize such issues, our proposed framework employs the CNN‐OHGS algorithm to reduce the distance variations among the vehicles thus ensuring the safety and optimal distance variation. Finally, the experimental analysis is conducted and the performance evaluation for various attacks, FID evaluation, and remorse function, and distance deviation for all sensor signal attacks are evaluated. The comparative analysis is made and we can clearly state that the proposed work has outperformed other existing approaches.