Search Results (346)

This paper introduces a robust and efficient Localized Spline-based Path-Planning (LSPP) algorithm designed to enhance autonomous vehicle navigation on highways. The LSPP approach prioritizes smooth maneuvering, obstacle avoidance, passenger comfort, and adherence to road constraints, including lane boundaries, through optimized trajectory generation using quintic spline functions and a dynamic speed profile. Leveraging real-time data from the vehicle’s sensor fusion module, the LSPP algorithm accurately interprets the positions of surrounding vehicles and obstacles, creating a safe, dynamically feasible path that is relayed to the Model Predictive Control (MPC) track-following module for precise execution. The theoretical distinction of LSPP lies in its modular integration of: (1) a finite state machine (FSM)-based decision-making layer that selects maneuver-specific goal states (e.g., keep lane, change lane left/right); (2) quintic spline optimization to generate smooth, jerk-minimized, and kinematically consistent trajectories; (3) a multi-objective cost evaluation framework that ranks competing paths according to safety, comfort, and efficiency; and (4) a closed-loop MPC controller to ensure real-time trajectory execution with robustness. Extensive simulations conducted in diverse highway scenarios and traffic conditions demonstrate LSPP’s effectiveness in delivering smooth, safe, and computationally efficient trajectories. Results show consistent improvements in lane-keeping accuracy, collision avoidance, enhanced materials wear performance, and planning responsiveness compared to traditional path-planning methods. These findings confirm LSPP’s potential as a practical and high-performance solution for autonomous highway driving. Full article

Autonomous vehicles (AVs) have emerged as a promising sustainable urban mobility solution, expected to lead to enhanced road safety, smoother traffic flows, less traffic congestion, improved accessibility, better energy utilization and environmental performance, as well as more efficient passenger and freight transportation, in terms of time and cost, due to better fleet management and platooning. However, challenges also arise, mostly related to data privacy, security and cyber-security, high acquisition and infrastructure costs, accident liability, even possible increased traffic congestion and air pollution due to induced travel demand. This paper presents the results of a survey conducted among 654 residents who experienced an autonomous bus (AB) service in the city of Trikala, Greece, in order to assess their willingness to use (WTU) and willingness to pay (WTP) for ABs, through testing a range of factors based on a literature review. Results useful to policy-makers were extracted, such as that the intention to use ABs was mostly shaped by psychological factors (e.g., users’ perceptions of usefulness and safety, and trust in the service provider), while WTU seemed to be positively affected by previous experience in using ABs. In contrast, sociodemographic factors were found to have very little effect on the intention to use ABs, while apart from personal utility, users’ perceptions of how autonomous driving will improve the overall life standards in the study area also mattered. Full article

This study proposes a new method to improve the accuracy of car-following models in predicting the mobility of mixed traffic flow involving trucks and automated vehicles (AVs). A classification is developed to categorize car-following behaviors into eight distinct modes based on vehicle type (passenger car/truck) and autonomy level (human-driven vehicle [HDV]/AV) for parameter calibration and simulation. The car-following model parameters are calibrated based on the HighD dataset, and the models are selected through minimizing statistical error. A cellular-automaton-based simulation platform is implemented in MATLAB (R2023b), and a decision framework is developed for the simulation. Key findings demonstrate that mode-specific parameter calibration improves model accuracy, achieving an average error reduction of 80% compared to empirical methods. The simulation results reveal a positive correlation between the AV penetration rate and traffic flow stability, which consequently enhances capacity. Specifically, a full transition from 0% to 100% AV penetration increases traffic capacity by 50%. Conversely, elevated truck penetration rates degrade traffic flow stability, reducing the average speed by 75.37% under full truck penetration scenarios. Additionally, higher AV penetration helps stabilize traffic flow, leading to reduced speed fluctuations and lower emissions, while higher truck proportions contribute to higher emissions due to increased traffic instability. Full article

Traffic congestion constitutes a substantial global issue, adversely impacting economic productivity and quality of life, with associated costs estimated at approximately 2% of GDP in various nations. This systematic review investigates the application of machine learning (ML) in traffic congestion forecasting from 2010 to 2024, adhering to the PRISMA 2020 guidelines. A comprehensive search of three major databases (IEEE Xplore, SpringerLink, and ScienceDirect) yielded 9695 initial records, with 115 studies meeting the inclusion criteria following rigorous screening. Data extraction encompassed methodological approaches, ML techniques, traffic characteristics, and forecasting periods, with quality assessment achieving near-perfect inter-rater reliability (Cohen’s $\kappa$ = 0.89). Deep Neural Networks were the predominant technical approach (47%), with supervised learning being the most prevalent (57%). Classification tasks were the most common (42%), primarily addressing recurrent congestion scenarios (76%) and passenger vehicles (90%). The quality of publications was notably high, with 85% appearing in Q1-ranked journals, demonstrating exponential growth from minimal activity in 2010 to 18 studies in 2022. Significant research gaps persist: reinforcement learning is underutilized (8%), rural road networks are underrepresented (2%), and industry–academia collaboration is limited (3%). Future research should prioritize multimodal transportation systems, real-time adaptation mechanisms, and enhanced practical implementation to advance intelligent transportation systems (ITSs). This review was not registered because it focused on mapping the research landscape rather than intervention effects. Full article

The long-standing need for a modern public transportation system in Lebanon, a developing country of the Middle East with an almost exclusive dependence on costly and polluting passenger cars, has become more pressing in recent years due to the worsening economic crisis and the onset of hyperinflation. This study investigates the potential reductions in energy use, emissions, and costs from the possible introduction of natural gas, hybrid, and battery-electric buses compared to traditional diesel buses in local real driving conditions. Four operating conditions were considered including severe congestion, peak, off-peak, and bus rapid transit (BRT) operation. Battery-electric buses are found to be the best performers in any traffic operation, conditional on having clean energy supply at the power plant and significant subsidy of bus purchase cost. Natural gas buses do not provide significant greenhouse gas emission savings compared to diesel buses but offer substantial reductions in the emission of all major pollutants harmful to human health. Results also show that accounting for additional energy consumption from the use of climate-control auxiliaries in hot and cold weather can significantly impact the performance of all bus technologies by up to 44.7% for electric buses on average. Performance of all considered bus technologies improves considerably in free-flowing traffic conditions, making BRT operation the most beneficial. A vehicle mix of diesel, natural gas, and hybrid bus technologies is found most feasible for the case of Lebanon and similar developing countries lacking necessary infrastructure for a near-term transition to battery-electric technology. Full article

The trend towards shorter supply chains and home delivery has rapidly increased delivery van traffic. Consequently, in the 20 years prior to 2018, delivery traffic has increased by 71%, while passenger vehicles have increased only by 13%. This drastic change in traffic patterns presented new challenges to decision makers and fortunately coincided with changes in the automotive industry, i.e., the advent of automation. However, the design of a controller is not straightforward due to the complex and nonlinear vehicle dynamics and the nonlinear relationship between the controller, tracking error and trajectory. This paper proposes a novel hybrid artificial-intelligence-based lateral control system for an autonomous delivery van to address these challenges to achieve the lowest value of tracking error. The strategy consists of multiple simultaneously operating fuzzy controllers. Their output signals are optimally weighted by a genetic algorithm to determine the proper allocation of control signals for calculating the final steering angle. Six different scenarios are implemented to evaluate the algorithm. A comparative analysis is then performed with two alternative state-of-the-art methods: (i) manually weighted and (ii) geometrically weighted controllers. During the tests, the vehicle’s speed varied, and the roads considered ranged from simple roads to a series of curves. The results show that the proposed strategy leads to a reduction of up to 91.2% and 61.1% in tracking error compared to the manually and geometrically weighted alternatives, respectively. Full article

The Controller Area Network (CAN) bus has been implemented in most modern Vehicles. Various attacks can be launched against the CAN bus protocol because it is designed without security mechanisms. It is essential to develop a highly accurate intrusion detection system (IDS) for CAN bus attacks. We design an effective ensemble learning-based IDS scheme for detecting and classifying DoS, fuzzing, replay, and spoofing attacks. These are common CAN bus attacks that can threaten the safety of a vehicle’s driver, passengers, and pedestrians. For this purpose, we utilize supervised machine learning in combination with ensemble methods. We first perform data balancing and feature selection. We build and fine-tune random forest, Xtreme gradient boosting, and decision tree supervised learning models. We then combine these models with voting, stacking, and bagging ensemble methods. The results obtained demonstrate the effectiveness of the proposed scheme when trained on real-life CAN traffic datasets to detect and classify these four attacks. The stacking method achieved the highest performance in terms of accuracy, precision, recall, F1-score, and area-under-the-curve receiver operator characteristic (ROC-AUC). The stacking method outperformed other recently proposed methods with an F1-score, precision, recall, and accuracy of 0.993, 0.993, 0.993, and 0.986, respectively. Full article

The introduction of Bus Rapid Transit (BRT) systems has the potential to alleviate urban traffic congestion. However, in certain cities in China, the increasing prevalence of privately owned vehicles, combined with the underutilization of bus lanes due to infrequent bus departures, has contributed to heightened congestion in general lanes. The advent of Internet of Things (IoT) technology offers a promising opportunity to develop intelligent public transportation systems, facilitating efficient management through seamless information transmission to end devices. This paper presents an IoT-based shared bus lane (IoT-SBL) that integrates intersection information, real-time traffic queuing conditions, and bus location data to encourage passenger vehicles to utilize the bus lane. This encouragement can be communicated through traditional signaling methods or future Infrastructure-to-Vehicle (I2V) and Vehicle-to-Vehicle (V2V) communication technologies. To evaluate the effectiveness of the IoT-SBL strategy, we proposed a stochastic model that incorporates queuing effects and derived a series of performance metrics through model analysis. The experimental findings indicated that the IoT-SBL strategy significantly reduces vehicle queuing, decreases vehicle delays, enhances intersection throughput efficiency, and lowers fuel consumption compared to the traditional bus lane strategy. Full article

This research focuses on monitoring and analyzing air pollutant emissions, mainly from passenger vehicles, at a busy urban intersection with 19 traffic lanes at the junction of Thivon Avenue and Iera Odos, located in the Egaleo municipality, an urban region of Athens, Greece. To collect data, a monitoring study was conducted specifically on the four central traffic streams of this specific intersection. On each segment of the road, a specific length was assigned through which vehicles pass at an average speed in order for their emissions to be estimated. For each vehicle, the engine type (gas or diesel) and engine displacement were taken into account to calculate the predicted mass of vehicle emissions. These measurements were conducted separately for each segment and recorded during three signal phases (from green to red) for two weekdays and one non-working day. This approach allows pollutant levels to be monitored at various hours and under various traffic conditions. The analysis revealed not only the overall quantity of emissions from vehicles but also their fluctuations throughout the day and traffic conditions, comparing them with the regulatory limits set by the EU. Significant findings regarding the impact of traffic on air quality are highlighted. Full article

The emergence of autonomous vehicles offers the potential to eliminate traditional traffic lanes, enabling vehicles to navigate freely in two-dimensional spaces. Unlike conventional traffic constrained by physical lanes, autonomous vehicles rely on real-time data exchange within platoons to adopt cooperative movement strategies, similar to synchronized flocks of birds. Motivated by this paradigm, this paper introduces an innovative traffic flow model based on the principles of particle swarm intelligence. In the proposed model, each vehicle within a platoon is treated as a particle contributing to the collective dynamics of the system. The motion of each vehicle is determined by the following two key factors: its local optimal velocity, influenced by the preceding vehicle, and its global optimal velocity, derived from the average of the optimal velocities of M vehicles within its observational range. To implement this framework, we develop a novel particle swarm optimization algorithm for autonomous vehicles and rigorously analyze its stability using linear system stability theory, as well as evaluate the system’s performance through four distinct indices inspired by traditional control theory. Numerical simulations are conducted to validate the theoretical assumptions of the model. The results demonstrate strong consistency between the proposed swarm intelligent model and the Bando model, providing evidence of its effectiveness. Additionally, the simulations reveal that the stability of the traffic flow system is primarily governed by the learning parameters $c_1$ and $c_2$, as well as the field of view parameter M. These findings underscore the potential of the swarm intelligent model to improve traffic flow system dynamics and contribute to the broader application of autonomous traffic systems management. In addition, it is worth noting that this paper explores the operational control of an AV platoon from a theoretical perspective, without fully considering passenger comfort, as well as “soft” instabilities (vehicles joining/leaving) and “hard” instabilities (technical failures/accidents). Future research will expand on these related aspects. Full article

With the prosperity of the economy and the continuous expansion of the port area, container trucks have become the main means of transportation on port roads. Traditional traffic flow research mainly focuses on passenger cars. In view of the unique characteristics of container truck traffic flow and the lack of research on conflict-influencing factors for this traffic flow, this paper is committed to filling this research gap. This paper uses drones and YOLOv8 technology to construct a vehicle trajectory dataset in the container truck traffic flow scenario and extracts relevant features of container truck traffic flow from vehicle trajectory data from a macro perspective. For the trajectory data after denoising, the time to collision (TTC) indicator is used to identify conflict events, and then the synthetic minority oversampling technique (SMOTE) is used to obtain four datasets. Machine learning and related classification models are selected for conflict prediction. It is worth noting that the XGBoost model performs better than other models on the four datasets, with an accuracy of 0.86 and an AUC value of 0.933. The Shapley additive explanation (SHAP) theory is used to explain and analyze the model results and compare them with existing studies. The results show that in container truck traffic flow, traffic density is the most important factor affecting conflicts, and conflicts occur more frequently when the traffic density is between 50 and 70 vehicles/km, followed by lane change rate. In contrast, for general traffic flows, studies have shown that speed is the main factor affecting conflicts. Full article

With the development and application of artificial intelligence (AI) technology, autonomous driving systems are gradually being applied on the road. However, people still have requirements for the safety and reliability of unmanned vehicles. Autonomous driving systems in today’s unmanned vehicles also have to respond to information security attacks. If they cannot defend against such attacks, traffic accidents might be caused, leaving passengers exposed to risks. Therefore, we investigated adversarial attacks on the traffic sign recognition of autonomous vehicles in this study. We used You Look Only Once (YOLO) to build a machine learning model for traffic sign recognition and simulated attacks on traffic signs. The simulated attacks included LED light strobes, color-light flash, and Gaussian noise. Regarding LED strobes and color-light flash, translucent images were used to overlay the original traffic sign images to simulate corresponding attack scenarios. In the Gaussian noise attack, Python 3.11.10 was used to add noise to the original image. Different attack methods interfered with the original machine learning model to a certain extent, hindering autonomous vehicles from recognizing traffic signs and detecting them accurately. Full article

Vehicle color coatings have long been recognized as a factor influencing road safety, particularly regarding their impact on speed perception and crash risk. This study aims to examine how different vehicle color coatings affect drivers’ perception of speed under nighttime driving conditions, with a specific focus on sustainability and visibility. A controlled laboratory experiment was conducted using a driving simulator to replicate realistic night traffic scenarios. A total of 161 participants evaluated passenger vehicles in four distinct color treatments, white (high-reflective paint), yellow (matte safety film), blue (glossy metallic finish), and black (low-reflective coating), at two speeds: 30 km/h and 50 km/h. Participants’ perceived speeds were collected and analyzed using standardized statistical methods. Results indicated a consistent pattern: speed was overestimated at 30 km/h and underestimated at 50 km/h across all vehicle colors. Lighter-colored vehicles (white and yellow) were perceived as moving faster than darker-colored vehicles (blue and black), with significant differences between black and yellow (30 km/h), yellow and blue (30 km/h), and black and white (50 km/h). Additionally, female participants tended to estimate higher speeds than male participants across most conditions. Other individual factors, such as place of residence, driver’s license type, driving experience, and frequency of driving, also showed measurable effects on speed perception. By using a simulator and accounting for diverse demographic characteristics, the study highlights how perceptual biases related to vehicle color can influence driver behavior. These findings emphasize the importance of considering vehicle color in traffic safety strategies, including driver education, vehicle design, and policy development aimed at reducing crash risk. Full article

The main objective of the research presented in this article was to analyze the composition of exhaust gases from passenger cars undergoing periodic inspections and to determine the influence of vehicle age, mileage and the applicable EURO emission standard on the level of emissions of individual components of exhaust gases and thus on the environment. The research was carried out at the District Vehicle Inspection Station in Nowy Sącz, using methods for analyzing the composition of exhaust gases and smoke opacity. The results obtained make it possible to assess whether exhaust emission diagnostics can form the basis for the implementation of a sustainable road transport policy. The study showed that older vehicles emit higher concentrations of carbon monoxide (CO) and hydrocarbons (HC), and diesel cars manufactured before 2010 are characterized by increased smoke opacity. A reliable analysis of the emissions performance of vehicles on the road enables more effective measures to be taken to reduce emissions and improve air quality through regulation, the introduction of clean traffic zones and raising environmental awareness among drivers. This is especially important in regions with specific geographical conditions, such as the Nowy Sącz district, where the terrain—Nowy Sącz is located in a basin surrounded by mountain ranges—favors the accumulation of pollutants and hinders the natural air circulation, leading to the long-term persistence of smog. Full article

The expected growth of electric vehicle (EV) usage will not only increase the energy demand but also bring the requirement to provide the necessary electrical infrastructure to handle the load. While charging infrastructure is becoming increasingly present in urban areas, special attention is required for transit traffic, not just for passengers but also for freight transport. Differences in the nature of battery charging compared to that of classical refueling require careful planning in order to provide a resilient electrical infrastructure that will supply enough energy at critical locations during peak hours. This paper presents a hybrid simulation model for analyzing fast-charging demand based on traffic flow, projected EV adoption, battery characteristics, and environmental conditions. The model integrates a probabilistic model for evaluating the charging requirements based on traffic flows with a discrete-event simulation (DES) framework to analyze charger utilization, waiting queues, and energy demand. The presented case of traffic flow on Slovenian motorways explored the expected power demands at various seasonal traffic intensities. The findings provide valuable insight for planning the charging infrastructure, the electrical grid, and also the layout by anticipating the number of vehicles seeking charging services. The modular design of the model allowed replacing key parameters with different traffic projections, supporting a robust scenario analysis and adaptive infrastructure planning. Replacing the parameters with real-time data opens the path for integration into a digital twin framework of individual EV charging hubs, providing the basis for development of an EV charging hub network digital twin. Full article