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On-ramp merging presents a critical challenge in autonomous driving, as vehicles from merging lanes need to dynamically adjust their positions and speeds while monitoring traffic on the main road to prevent collisions. To address this challenge, we propose a novel merging control scheme based on reinforcement learning, which integrates lateral control mechanisms. This approach ensures the smooth integration of vehicles from the merging lane onto the main road, optimizing both fuel efficiency and passenger comfort. Furthermore, we recognize the impact of vehicle-to-vehicle (V2V) communication on control strategies and introduce an enhanced protocol leveraging Cellular Vehicle-to-Everything (C-V2X) Mode 4. This protocol aims to reduce the Age of Information (AoI) and improve communication reliability. In our simulations, we employ two AoI-based metrics to rigorously assess the protocol's effectiveness in autonomous driving scenarios. By combining the NS3 network simulator with Python, we simulate V2V communication and vehicle control simultaneously. The results demonstrate that the enhanced C-V2X Mode 4 outperforms the standard version, while the proposed control scheme ensures safe

Vehicle edge caching is a promising technology that can significantly reduce the latency for vehicle users (VUs) to access content by pre-caching user-interested content at edge nodes. It is crucial to accurately predict the content that VUs are interested in without exposing their privacy. Traditional federated learning (FL) can protect user privacy by sharing models rather than raw data. However, the training of FL requires frequent model transmission, which can result in significant communication overhead. Additionally, vehicles may leave the road side unit (RSU) coverage area before training is completed, leading to training failures. To address these issues, in this paper, we propose a personalized federated distillation assisted vehicle edge caching strategy. The simulation results demonstrate that the proposed vehicle edge caching strategy has good robustness to variations in vehicle speed, significantly reducing communication overhead.

The emergence of Software-Defined Vehicles represents a fundamental shift in automotive design, prioritizing software-centric architectures over traditional hardware-driven models. SDVs require modularity, interoperability, real-time processing, and over-the-air update capabilities throughout the vehicle lifecycle. However, current vehicle systems, characterized by tightly coupled software and hardware, struggle to meet these demands due to their complexity and heterogeneity. A critical first step toward enabling SDVs is the decoupling of software from hardware, which can be facilitated through a robust Hardware Abstraction Layer. While existing HALs offer hardware independence and standardized interfaces, their applicability and effectiveness in SDV contexts remain uncertain. This paper systematically evaluates current automotive HALs and explores HAL mechanisms from non-automotive domains, including smartphones, networking, and industrial automation, to extract cross-domain insights relevant to SDV development. A criteria-driven evaluation framework is developed to assess HALs against SDV-specific needs. Findings reveal that while middleware-based HALs offer portability and modul

This paper presents a framework to estimate the environmental impact of solar electric vehicles, accounting for the emissions caused by photovoltaic system production as well as vehicle use. We leverage a cradle-to-gate life cycle assessment to estimate the greenhouse gas emissions of the vehicle-integrated photovoltaic system, from the raw material extraction to the final panel assembly, including the effect of the electricity mix both at the factory location and in the country of use. %the vehicle's life cycle, considering both Furthermore, we modify an existing optimization framework for battery electric vehicles to optimally design a solar electric vehicle and estimate its energy consumption. We showcase our framework by analyzing a case study where the mono-crystalline silicon extraction and refinement processes occur in China, while the final assembly of the panel is in The Netherlands, generating 118 kg of CO2 equivalents per square meter of solar panel. The results suggest that it is generally beneficial to operate solar electric vehicles in countries with a high irradiation index. However, when the local electricity mix already displays a low carbon intensity, the addition

Navigation is a very crucial aspect of autonomous vehicle ecosystem which heavily relies on collecting and processing large amounts of data in various states and taking a confident and safe decision to define the next vehicle maneuver. In this paper, we propose a novel architecture based on Quantum Artificial Intelligence by enabling quantum and AI at various levels of navigation decision making and communication process in Autonomous vehicles : Quantum Neural Networks for multimodal sensor fusion, Nav-Q for Quantum reinforcement learning for navigation policy optimization and finally post-quantum cryptographic protocols for secure communication. Quantum neural networks uses quantum amplitude encoding to fuse data from various sensors like LiDAR, radar, camera, GPS and weather etc., This approach gives a unified quantum state representation between heterogeneous sensor modalities. Nav-Q module processes the fused quantum states through variational quantum circuits to learn optimal navigation policies under swift dynamic and complex conditions. Finally, post quantum cryptographic protocols are used to secure communication channels for both within vehicle communication and V2X (Vehic

For the autonomous operation of articulated vehicles at distribution centers, accurate positioning of the vehicle is of the utmost importance. Automation of these vehicle poses several challenges, e.g. large swept path, asymmetric steering response, large slide slip angles of non-steered trailer axles and trailer instability while reversing. Therefore, a validated vehicle model is required that accurately and efficiently predicts the states of the vehicle. Unlike forward driving, open-loop validation methods can not be used for reverse driving of articulated vehicles due to their unstable dynamics. This paper proposes an approach to stabilize the unstable pole of the system and compares three vehicle models (kinematic, non-linear single track and multibody dynamics model) against real-world test data obtained from low-speed experiments at a distribution center. It is concluded that single track non-linear model has a better performance in comparison to other models for large articulation angles and reverse driving maneuvers.

Automated parking requires accurate localization for quick and precise maneuvering in tight spaces. While the longitudinal velocity can be measured using wheel encoders, the estimation of the lateral velocity remains a key challenge due to the absence of dedicated sensors in consumer-grade vehicles. Existing approaches often rely on simplified vehicle models, such as the zero-slip model, which assumes no lateral velocity at the rear axle. It is well established that this assumption does not hold during low-speed driving and researchers thus introduce additional heuristics to account for differences. In this work, we analyze real-world data from parking scenarios and identify a systematic deviation from the zero-slip assumption. We provide explanations for the observed effects and then propose a lateral velocity model that better captures the lateral dynamics of the vehicle during parking. The model improves estimation accuracy, while relying on only two parameters, making it well-suited for integration into consumer-grade applications.

Co-optimization of both vehicle speed and gear position via model predictive control (MPC) has been shown to offer benefits for fuel-efficient autonomous driving. However, optimizing both the vehicle's continuous dynamics and discrete gear positions may be too computationally intensive for a real-time implementation. This work proposes a learning-based MPC scheme to address this issue. A policy is trained to select and fix the gear positions across the prediction horizon of the MPC controller, leaving a significantly simpler continuous optimization problem to be solved online. In simulation, the proposed approach is shown to have a significantly lower computation burden and a comparable performance, with respect to pure MPC-based co-optimization.

Vehicle platooning is an important technology in modern transportation systems, offering significant improvements in highway traffic efficiency and fuel economy. Achieving coordinated behavior among vehicles in a platoon depends on wireless communication. However, packet losses in wireless communication can create critical safety issues when they occur together with sudden braking. In this paper, we propose a rigorous simulation-based method for studying such safety issues by analyzing the minimum inter-vehicle distance over time across control parameters that guarantee string stability. In particular, our method computes the exact distance at simulation time instants and guarantees that the change in distance between consecutive simulation time instants remains bounded. Therefore, the distances obtained at simulation times are representative of the continuous-time behavior, and the distances between those times can be accurately approximated. Our derivation relies on a lifted state representation and differential inequalities. For the proposed simulation method, we provide two approaches for selecting simulation times to ensure that the error in distance approximation remains with

Federated Learning (FL) can protect the privacy of the vehicles in vehicle edge computing (VEC) to a certain extent through sharing the gradients of vehicles' local models instead of local data. The gradients of vehicles' local models are usually large for the vehicular artificial intelligence (AI) applications, thus transmitting such large gradients would cause large per-round latency. Gradient quantization has been proposed as one effective approach to reduce the per-round latency in FL enabled VEC through compressing gradients and reducing the number of bits, i.e., the quantization level, to transmit gradients. The selection of quantization level and thresholds determines the quantization error, which further affects the model accuracy and training time. To do so, the total training time and quantization error (QE) become two key metrics for the FL enabled VEC. It is critical to jointly optimize the total training time and QE for the FL enabled VEC. However, the time-varying channel condition causes more challenges to solve this problem. In this paper, we propose a distributed deep reinforcement learning (DRL)-based quantization level allocation scheme to optimize the long-term

Detecting and characterising vehicles is one of the purposes of embedded systems used in intelligent environments. An analysis of a vehicle characteristics can reveal inappropriate or dangerous behaviour. This detection makes it possible to sanction or notify emergency services to take early and practical actions. Vehicle detection and characterisation systems employ complex sensors such as video cameras, especially in urban environments. These sensors provide high precision and performance, although the price and computational requirements are proportional to their accuracy. These sensors offer high accuracy, but the price and computational requirements are directly proportional to their performance. This article introduces a system based on modular devices that is economical and has a low computational cost. These devices use ultrasonic sensors to detect the speed and length of vehicles. The measurement accuracy is improved through the collaboration of the device modules. The experiments were performed using multiple modules oriented to different angles. This module is coupled with another specifically designed to detect distance using previous modules speed and length data. The

This paper addresses the challenge of decentralized task allocation within heterogeneous multi-agent systems operating under communication constraints. We introduce a novel framework that integrates graph neural networks (GNNs) with a centralized training and decentralized execution (CTDE) paradigm, further enhanced by a tailored Proximal Policy Optimization (PPO) algorithm for multi-agent deep reinforcement learning (MARL). Our approach enables unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) to dynamically allocate tasks efficiently without necessitating central coordination in a 3D grid environment. The framework minimizes total travel time while simultaneously avoiding conflicts in task assignments. For the cost calculation and routing, we employ reservation-based A* and R* path planners. Experimental results revealed that our method achieves a high 92.5% conflict-free success rate, with only a 7.49% performance gap compared to the centralized Hungarian method, while outperforming the heuristic decentralized baseline based on greedy approach. Additionally, the framework exhibits scalability with up to 20 agents with allocation processing of 2.8 s and robustne

Distributed model predictive control (MPC) is currently being investigated as a solution to the important control challenge presented by networks of hybrid dynamical systems. However, a benchmark problem for distributed hybrid MPC is absent from the literature. We propose distributed control of a platoon of autonomous vehicles as a comparison benchmark problem. The problem provides a complex and adaptable case study, upon which existing and future approaches to distributed MPC for hybrid systems can be evaluated. Two hybrid modeling frameworks are presented for the vehicle dynamics. Five hybrid MPC controllers are then evaluated and extensively assessed on the fleet of vehicles. Finally, we comment on the need for new efficient and high performing distributed MPC schemes for hybrid systems.

Predicting the future trajectories of surrounding vehicles based on their history trajectories is a critical task in autonomous driving. However, when small crafted perturbations are introduced to those history trajectories, the resulting anomalous (or adversarial) trajectories can significantly mislead the future trajectory prediction module of the ego vehicle, which may result in unsafe planning and even fatal accidents. Therefore, it is of great importance to detect such anomalous trajectories of the surrounding vehicles for system safety, but few works have addressed this issue. In this work, we propose two novel methods for learning effective and efficient representations for online anomaly detection of vehicle trajectories. Different from general time-series anomaly detection, anomalous vehicle trajectory detection deals with much richer contexts on the road and fewer observable patterns on the anomalous trajectories themselves. To address these challenges, our methods exploit contrastive learning techniques and trajectory semantics to capture the patterns underlying the driving scenarios for effective anomaly detection under supervised and unsupervised settings, respectively

This paper presents a novel energy-efficient motion planning algorithm for Connected Autonomous Vehicles (CAVs) on urban roads. The approach consists of two components: a decision-making algorithm and an optimization-based trajectory planner. The decision-making algorithm leverages Signal Phase and Timing (SPaT) information from connected traffic lights to select a lane with the aim of reducing energy consumption. The algorithm is based on a heuristic rule which is learned from human driving data. The optimization-based trajectory planner generates a safe, smooth, and energy-efficient trajectory toward the selected lane. The proposed strategy is experimentally evaluated in a Vehicle-in-the-Loop (VIL) setting, where a real test vehicle receives SPaT information from both actual and virtual traffic lights and autonomously drives on a testing site, while the surrounding vehicles are simulated. The results demonstrate that the use of SPaT information in autonomous driving leads to improved energy efficiency, with the proposed strategy saving 37.1% energy consumption compared to a lane-keeping algorithm.

The vehicle recognition area, including vehicle make-model recognition (VMMR), re-id, tracking, and parts-detection, has made significant progress in recent years, driven by several large-scale datasets for each task. These datasets are often non-overlapping, with different label schemas for each task: VMMR focuses on make and model, while re-id focuses on vehicle ID. It is promising to combine these datasets to take advantage of knowledge across datasets as well as increased training data; however, dataset integration is challenging due to the domain gap problem. This paper proposes ATEAM, an annotation team-of-experts to perform cross-dataset labeling and integration of disjoint annotation schemas. ATEAM uses diverse experts, each trained on datasets that contain an annotation schema, to transfer knowledge to datasets without that annotation. Using ATEAM, we integrated several common vehicle recognition datasets into a Knowledge Integrated Dataset (KID). We evaluate ATEAM and KID for vehicle recognition problems and show that our integrated dataset can help off-the-shelf models achieve excellent accuracy on VMMR and vehicle re-id with no changes to model architectures. We achieve

Teleoperation is becoming an essential feature in automated vehicle concepts, as it will help the industry overcome challenges facing automated vehicles today. Teleoperation follows the idea to get humans back into the loop for certain rare situations the automated vehicle cannot resolve. Teleoperation therefore has the potential to expand the operational design domain and increase the availability of automated vehicles. This is especially relevant for concepts with no backup driver inside the vehicle. While teleoperation resolves certain issues an automated vehicle will face, it introduces new challenges in terms of safety requirements. While safety and regulatory approval is a major research topic in the area of automated vehicles, it is rarely discussed in the context of teleoperated road vehicles. The focus of this paper is to systematically analyze the potential hazards of teleoperation systems. An appropriate hazard analysis method (STPA) is chosen from literature and applied to the system at hand. The hazard analysis is an essential part in developing a safety concept (e.g., according to ISO26262) and thus far has not been discussed for teleoperated road vehicles.

3D object detection at long range is crucial for ensuring the safety and efficiency of self driving vehicles, allowing them to accurately perceive and react to objects, obstacles, and potential hazards from a distance. But most current state of the art LiDAR based methods are range limited due to sparsity at long range, which generates a form of domain gap between points closer to and farther away from the ego vehicle. Another related problem is the label imbalance for faraway objects, which inhibits the performance of Deep Neural Networks at long range. To address the above limitations, we investigate two ways to improve long range performance of current LiDAR based 3D detectors. First, we combine two 3D detection networks, referred to as range experts, one specializing at near to mid range objects, and one at long range 3D detection. To train a detector at long range under a scarce label regime, we further weigh the loss according to the labelled point's distance from ego vehicle. Second, we augment LiDAR scans with virtual points generated using Multimodal Virtual Points (MVP), a readily available image-based depth completion algorithm. Our experiments on the long range Argovers

This paper examines the degree to which connectivity and automation can potentially reduce the overall fuel consumption of on-road vehicles. The paper begins with a simulation study highlighting the tradeoff between: (i) the fuel that a vehicle can save through speed trajectory shaping, versus (ii) the additional inter-vehicle spacing needed for this trajectory shaping to be feasible. This study shows that connectivity and automation are essential, rather than merely useful, for substantial reductions in the fuel consumed by fixed on-road vehicle powertrain/chassis configurations in traffic. Motivated by this insight, we survey the literature on the fuel savings achievable through different connected/automated vehicle technologies. This includes optimal vehicle routing, eco-arrival/departure at intersections, platooning, speed trajectory optimization, predictive driveline disengagement, predictive gear shifting, and predictive powertrain accessory control. This survey shows that the ability to shape vehicle speed trajectories collaboratively plays a dominant role in reducing urban/suburban fuel consumption, while platooning plays a dominant role in influencing the attainable fuel s