Search Results (55)

Advanced driving technologies have the potential to transform the transportation sector. Specifically, the progress of autonomous vehicles (AVs) has caught the interest of governmental authorities, industrial groups, and academic institutions, with the goal of improving the driving experience, effectiveness, and comfort while also improving safety and flexibility and lowering vehicle emissions. Considering these facts, the purpose of this study is to assess the possible effects and advantages of AVs under diverse traffic situations in urban and rural environments. Knowledge of traffic behavior inside a certain road network is made easier by traffic microsimulation. PTV VISSIM (Verkehr In Städten—SIMulationsmodell) is among the microsimulation software programs that has attracted great interest because of its remarkable capacity to faithfully simulate traffic conditions. This review helps researchers choose the best methodological strategy for their individual study objectives and restrictions while using VISSIM. This research assesses the effect of AVs in different driving behavior and weather conditions in urban and rural situations using VISSIM and introduces traffic safety using the surrogate safety assessment model (SSAM). The study focuses on 10 parameters from the Wiedemann 99 car-following model and speed distribution to establish the correlation between weather conditions and surrogate safety measures (SSMs). The findings could lead to more accurate and authentic models of driving behavior and encourage the automotive industry to further equip AVs to operate efficiently in various environmental and driving conditions. Full article

Machine-to-machine (M2M) communications are becoming the most important factor shaping network traffic. However, traditional controls developed for human-generated traffic are not able to cope with new demands. Thus, hybrid MAC protocols have been proposed to make use of the combined advantages of contention and reservation. Most of them are based on a contention stage (where a variant of CSMA/CA or ALOHA is used) followed by a reservation stage (e.g., TDMA or FDMA). In this paper, we introduce 2CA-R², a hybrid MAC protocol for M2M communications intended to be used in the device domain. What distinguishes this proposal is that the contention stage is controlled by a conflict–resolution algorithm known as Adaptive-2C. The protocol was evaluated using a model based on a Markov chain and computer simulations. Its performance was compared with DCF, the MAC technique used in IEEE802.11 standards. Our results show significant improvements over DCF in various metrics of network performance across different traffic situations. We also evaluated the time the protocol takes to resolve an access conflict, and we observed substantial improvements in the number of stations that can be served with the same network resource (in some cases, around a 40% improvement). Full article

This study proposes an advanced intelligent vehicle path-following control system using deep reinforcement learning, with a particular focus on the role of road geometry perception in motion planning and control. The system is structured around a three-degree-of-freedom (3-DOF) vehicle model, which facilitates the extraction of critical dynamic features necessary for robust control. The longitudinal control architecture integrates a Deep Deterministic Policy Gradient (DDPG) agent to optimise longitudinal velocity and acceleration, while lateral vehicle control is handled by a Deep Q-Network (DQN). To enhance situational awareness and adaptability, the system incorporates key input variables, including ego vehicle speed, speed error, lateral deviation, lateral error, and safety distance to the preceding vehicle, all in the context of road geometry and vehicle dynamics. In addition, the influence of road curvature is embedded into the control framework through perceived acceleration (sensed by vehicle occupants), allowing for more accurate and responsive adaptation to varying road conditions. The vehicle control system is tested in a simulated environment with a lead car in front with realistic speed profiles. The system outputs continuous values for acceleration and steering angle. The results of this study suggest that the proposed intelligent control system not only improves driver assistance but also has potential applications in autonomous driving. This framework contributes to the development of more autonomous, efficient, safety-aware, and comfortable vehicle control systems. Full article

The risk of serious crashes is notably higher among young and novice drivers. This increased risk is due to several factors, including a lack of recognition of dangerous situations, an overestimation of driving skills, and vulnerability to peer pressure. Recently, advanced driver assistance systems (ADAS) have been integrated into vehicles to help mitigate crashes linked to these factors. While numerous studies have examined ADAS broadly, few have specifically investigated its effects on young and novice drivers. This study aimed to address that gap by exploring ADAS’s impact on these drivers. Most studies in this review conclude that ADAS is beneficial for young and novice drivers, though some research suggests its impact may be limited or even negligible. Tailoring ADAS to address the unique needs of young drivers could enhance both the system’s acceptance and reliability. The review also found that unimodal warnings (e.g., auditory or visual) are as effective as multimodal warnings. Of the different types of warnings, auditory and visual signals proved the most effective. Additionally, ADAS can influence young drivers’ car-following behavior; for instance, drivers may maintain greater safety buffers or drive closely to avoid alarm triggers, likely due to perceived system unreliability. Aggressive drivers tend to benefit most from active ADAS, which actively intervenes to assist the driver. Future research could explore the combined effects of multiple ADAS functions within a single vehicle on young and novice drivers to better understand how these systems interact and impact driver behavior. Full article

The use of traffic simulation to analyze traffic safety and performance has become common in transportation engineering. Microsimulation methods are increasingly used to analyze driving performance for different road geometries and environmental elements. Drivers’ perception has an important impact on driving performance factors contributing to traffic safety on transportation facilities (highways, arterials, intersections, etc.). Impaired vision leads to failure in drivers’ perception and making right decisions. Various studies investigated the impact of environmental elements (fog, rain, snow, etc.) on driving performance. However, there is limited research examining the potentially detrimental effects on driving capabilities due to differing exposure to natural light brightness, in particular sun exposure. Autonomous vehicles (AVs) showed a significant impact enhancing traffic capacity and improving safety margins in car-following models. AVs may also enhance and/or complement human driving under deteriorated driving conditions such as sun glare. This study uses a calibrated traffic simulation and surrogate safety assessment model to improve traffic operations and safety performance under impaired visibility using different types of autonomous vehicles. A combination of visibility reduction, traffic flow characteristics, and autonomy levels of AVs was simulated and assessed in terms of the number of conflicts, severity level, and traffic operations. The simulation analysis results used to reveal the contribution of conflicts to the risk of crashes varied based on the influence of autonomy level on safe driving during sun glare exposure. The outcome of this study indicates the benefits of using different levels of AVs as a solution to driving under vision impairment situations that researchers, traffic engineers, and policy makers can use to enhance traffic operation and road safety in urban areas. Full article

Since the field of autonomous vehicles is developing quickly, it is becoming increasingly crucial for them to safely and effectively navigate their surroundings to avoid collisions. The primary collision avoidance algorithms currently employed by self-driving cars are examined in this thorough survey. It looks into several methods, such as sensor-based methods for precise obstacle identification, sophisticated path-planning algorithms that guarantee cars follow dependable and safe paths, and decision-making systems that allow for adaptable reactions to a range of driving situations. The survey also emphasizes how Machine Learning methods can improve the efficacy of obstacle avoidance. Combined, these techniques are necessary for enhancing the dependability and safety of autonomous driving systems, ultimately increasing public confidence in this game-changing technology. Full article

Improving the ability of autonomous vehicles to accurately identify and follow lanes in various contexts is crucial. This project aims to provide a novel framework for optimizing a self-driving vehicle that can detect lanes and steer accordingly. A virtual sandbox environment was developed in Unity3D that provides a semi-automated procedural road and driving generation framework for a variety of road scenarios. Four types of segments replicate actual driving situations by directing the car using strategically positioned waypoints. A training dataset thus generated was used to train a behavioral driving model that employs a convolutional neural network to detect the lane and ensure that the car steers autonomously to remain within lane boundaries. The model was evaluated on real-world driving footage from Comma.ai, exhibiting an autonomy of 77% in low challenge road conditions and of 66% on roads with sharper turns. Full article

In today’s power systems, electric vehicles (EVs) constitute a significant factor influencing electricity dynamics, with their important role anticipated in future smart grid systems. An important feature of electric vehicles is their dual capability to both charge and discharge energy to/from their battery storage. Notably, the discharge capability enables them to offer vehicle-to-grid (V2G) services. However, most V2G research focuses on passenger cars, which typically already have their own specific usage purposes and various traveling schedules. This situation may pose practical challenges in providing ancillary services to the grid. Conversely, electric school buses (ESBs) exhibit a more predictable usage pattern, often deployed at specific times and remaining idle for extended periods. This makes ESBs more practical for delivering V2G services, especially when prompted by incentive price signals from grid or utility companies (UC) requesting peak shaving services. In this paper, we introduce a V2G energy sharing model focusing on ESBs in various schools in a single community by formulating the problem as a leader–follower game. In this model, the UC assumes the role of the leader, determining the optimal incentive price to offer followers for discharging energy from their battery storage. The UC aims to minimize additional costs from generating energy during peak demand. On the other hand, schools in a community possessing multiple ESBs act as followers, seeking the optimal quantity of discharged energy from their battery storage. They aim to maximize utility by responding to the UC’s incentive price. The results demonstrate that the proposed model and algorithm significantly aid the UC in reducing the additional cost of energy generation during peak periods by 36% compared to solely generating all electricity independently. Furthermore, they substantially reduce the utility bills for schools by up to 22.6% and lower the peak-to-average ratio of the system by up to 9.5%. Full article

Vehicular ad hoc networks (VANETs) can bolster road safety through the proactive dissemination of emergency messages (EMs) among vehicles, effectively reducing the occurrence of traffic-related accidents. It is difficult to transmit EMs quickly and reliably due to the high-speed mobility of VANET and the attenuation of the wireless signal. However, poor network design and high vehicle mobility are the two most difficult problems that affect VANET’s network performance. The real-time traffic situation and network dependability will also be significantly impacted by route selection and message delivery. Many of the current works have undergone studies focused on forwarder selection and message transmission to address these problems. However, these earlier approaches, while effective in forwarder selection and routing, have overlooked the critical aspects of communication overhead and excessive energy consumption, resulting in transmission delays. To address the prevailing challenges, the proposed solutions use edge computing to process and analyze data locally from surrounding cars and infrastructure. EDGE-RSUs are positioned by the side of the road. In intelligent transportation systems, this lowers latency and enhances real-time decision-making by employing proficient forwarder selection techniques and optimizing the dissemination of EMs. In the context of 5G-enabled VANET, this paper introduces a novel routing protocol, namely, the supercluster-based urban multi-hop broadcast and best forwarder selection protocol (UMB-BFS). The improved twin delay deep deterministic policy gradient (IT3DPG) method is used to select the target region for emergency message distribution after route selection. Clustering is conducted using modified density peak clustering (MDPC). Improved firefly optimization (IFO) is used for optimal path selection. In this way, all emergency messages are quickly disseminated to multiple directions and also manage the traffic in VANET. Finally, we plotted graphs for the following metrics: throughput (3.9 kbps), end-to-end delay (70), coverage (90%), packet delivery ratio (98%), packet received (12.75 k), and transmission delay (57 ms). Our approach’s performance is examined using numerical analysis, demonstrating that it performs better than the current methodologies across all measures. Full article

This study aims to examine the differences in drivers’ reaction time (RTs) while driving on horizontal curves and straight roadway segments, among different driver classes, and in different driving environments to better understand human driver behavior in typical car-following situations. Therefore, behavioral measures were extracted from naturalistic car-following trajectories to estimate the RT. The RT was estimated for two stimulus–response pairs, namely, the speed–gap and relative speed–acceleration pairs, by using the cross-classification method. The RT was estimated separately for each driver and aggregated based on location and based on driver class. The results reveal that drivers’ RTs on curves are consistently higher than their RTs on straight segments, and this difference is statistically significant. The comparison between normal drivers and aggressive drivers indicates that regardless of the location, aggressive drivers have a significantly longer RT than normal drivers, as aggressive drivers can accept closer gaps and higher relative speed. Also, cautious drivers have a longer RT compared with normal drivers; however, the difference is not significant in most cases. Furthermore, cautious and normal drivers have longer RTs on curves compared with their RTs on straight segments. Additionally, the RT on rural horizontal curves is longer than the RT on urban curves, yet the differences are insignificant. Full article

The significance of sustainable mobility transition projects extends beyond borders. Transportation, particularly passenger vehicles, is a crucial sector in achieving sustainability. Therefore, prioritizing sustainable green mobility has led to the inclusion of alternative solutions, with a focus on accelerating the shift towards electric vehicle (EV) technologies and implementing a ban on the sale of new petrol and diesel cars in all European countries by 2035. Italy has been making progress in this area as the country seeks to address environmental concerns, reduce emissions, and promote sustainable transportation. However, compared to other European countries in 2024, Italy still has a long way to go to achieve a sustainable market share. In this regard, this article aims to address several questions related to the promotion and scaling up of the electric mobility transition project in Italy, taking into account the current situation. Specifically, it seeks to identify internal and external factors associated with this technology ecosystem, along with their relative importance. To conduct this study, a strengths, weaknesses, opportunities, and threats (SWOT) analysis was conducted to identify the factors, which was followed by the analytical hierarchy process (AHP) methodology to determine their priority and importance. A total of 8 internal factors and 14 external factors were analyzed, and their overall priority was determined. This study reveals that it is crucial to capitalize on the opportunities and strengths related to technology ecosystems while effectively mitigating the threats and technological limitations in order to scale up technology adoption. In particular, strengths S1 and S3 were given the highest overall priority scores, suggesting that they are the most important factors to leverage for the successful adoption of the technology. These prioritized factors and subfactors are crucial for expediting the transition process and can influence consumers’ decisions. However, without a substantial increase in consumer understanding and knowledge of these technologies, public education campaigns will be necessary. The significance of this study is paramount, and its results can contribute to the continuous enhancement in the formulation of practical plans and regulations to promote sustainable transportation, taking into account the identified factors. Full article

Musicological and traffic psychology research shows that emotions can be changed by certain tone combinations or sound characteristics and that emotions, in turn, influence our driving behavior. Nevertheless, there are no studies on how a dynamic active sound design could influence driving behavior via changing the emotional state of drivers in certain driving situations. Based on a previous study, emotionalizing sounds, characterized by their capacity to evoke specific emotional responses in individuals, were created and used to investigate their effect on the perception of safety distances in an online study. To test this, participants made statements on the safety distance shown in videos of cars following scenarios combined with emotionalizing sounds. The results show a significant difference in the estimated safety distance for videos combined with sounds invoking positive emotions like light-heartedness vs. sounds invoking negative emotions like feeling threatened. The odds of the safety distance being evaluated as too small compared with appropriate were two to three times higher for some threatening sounds vs. the positive sounds. The results further suggest that threatening sounds influenced participants’ wishes to increase the depicted safety distances. The results show that emotionalizing sounds had effects on the participants, though not all were statistically significant. Full article

Under the development of intelligent network technology, drivers can obtain the surrounding traffic situation in real time, which is conducive to improving the stability of traffic flow. Therefore, this paper proposes a new curve-car-following model considering multi-vehicle information of adjacent lanes in connected environment, and conducts linear and nonlinear stability analyses of the model to demonstrate the effectiveness of the proposed model and its ability to improve the stability of traffic system; in addition, numerical simulation experiments of traffic flow convoys are designed to analyze the effects of different parameters in the proposed model on the stability of the traffic flow and test the proposed model’s ability to maintain the following behavior in a convoy. Furthermore, numerical simulation experiments are designed to analyze the effects of different parameters in the proposed model on the stability of traffic flow, and to test the ability of the proposed model to maintain the following behavior in the convoy. The model can provide theoretical guidance to alleviate traffic congestion and improve safety, and extend the application of the following model in curved multi-lane road scenarios. Full article

The advent of autonomous vehicles (AVs) in the traffic stream is expected to innovatively prevent crashes resulting from human errors in manually driven vehicles (MVs). However, substantial safety benefits due to AVs are not achievable quickly because the mixed-traffic conditions in which AVs and MVs coexist in the current road infrastructure will continue for a considerably long period of time. The purpose of this study is to develop a methodology to evaluate the driving safety of mixed car-following situations between AVs and MVs on freeways based on a multi-agent driving-simulation (MADS) technique. Evaluation results were used to answer the question ‘What road condition would make the mixed car-following situations hazardous?’ Three safety indicators, including the acceleration noise, the standard deviation of the lane position, and the headway, were used to characterize the maneuvering behavior of the mixed car-following pairs in terms of driving safety. It was found that the inter-vehicle safety of mixed pairs was poor when they drove on a road section with a horizontal curve length of 1000 m and downhill slope of 1% or 3%. A set of road sections were identified, using the proposed evaluation method, as hazardous conditions for mixed car-following pairs consisting of AVs and MVs. The outcome of this study will be useful for supporting the establishment of safer road environments and developing novel V2X-based trafficsafetyinformation content that enables the enhancement of mixed-traffic safety. Full article

Postural control characteristics have been proposed as a predictor of Motion Sickness (MS). However, postural adaptation to sensory environment changes may also be critical for MS susceptibility. In order to address this issue, a postural paradigm was used where accurate orientation information from body sensors could be lost and restored, allowing us to infer sensory re-weighting dynamics from postural oscillation spectra in relation to car-sickness susceptibility. Seventy-one participants were standing on a platform (eyes closed) alternating from static phases (proprioceptive and vestibular sensors providing reliable orientation cues) to sway referenced to the ankle-angle phases (proprioceptive sensors providing unreliable orientation cues). The power spectrum density (PSD) on a 10 s sliding window was computed from the antero-posterior displacement of the center of pressure. Energy ratios (ERs) between the high (0.7–1.3 Hz) and low (0.1–0.7 Hz) frequency bands of these PSDs were computed on key time windows. Results showed no difference between MS and non-MS participants following loss of relevant ankle proprioception. However, the reintroduction of reliable ankle signals led, for the non-MS participants, to an increase of the ER originating from a previously up-weighted vestibular information during the sway-referenced situation. This suggests inter-individual differences in re-weighting dynamics in relation to car-sickness susceptibility. Full article