Search Results (120)

This study analyzed the impact of human drivers’ Keep Right Rule noncompliance on sustainable freeway operations in mixed traffic. Using the microscopic traffic simulation tool, a total of 36 scenarios were examined based on variations in driving behavior, presence of slow vehicles in the passing lane, desired speed, and number of lanes. The Wiedemann-99 car-following model and autonomous driving logic were applied for simulation. Simulation results revealed that the occupation of the passing lane by a human-driven slow vehicle increased the recovery time and variability in right-side rule compared to free lane selection. Also, 20 km/h was a threshold desired speed gap that activated the bottleneck by the slow vehicle in a passing lane. Lastly, as the number of lanes increased, bottleneck formation was diminished. The findings point to a mixed traffic systemic paradox. Human drivers can alleviate bottleneck formation by flexibly performing right-side overtaking even though it is illegal, whereas autonomous vehicles cannot perform right-side overtaking, which unintentionally activates a bottleneck under strict rule compliance. These results show that in mixed traffic conditions, even minor violations of traffic rules by human drivers can lead to congestion. Therefore, to achieve sustainable and safe road traffic by harmonizing mixed traffic, institutional improvements are necessary alongside advances in autonomous driving technology. Full article

In the centralized scheduling mode of automated container terminals, Automated Guided Vehicles (AGVs) often experience decision-making delays caused by system information-processing bottlenecks, which significantly affect path-planning efficiency and are particularly evident in sudden-traffic scenarios. To address this issue, this paper incorporates the artificial potential field (APF) into the cost function of Model Predictive Control (MPC) and develops a dual-trigger mechanism for lane-change and lane-return MPC obstacle-avoidance framework (Event-Triggered Model Predictive Control, EMPC). This framework integrates an obstacle-triggered local optimization mechanism and a lane-change trigger, enabling AGV to perform autonomous and dynamically responsive local obstacle avoidance, thereby improving local path-planning efficiency. Furthermore, a Proximal Policy Optimization (PPO)-based strategy is introduced to adaptively adjust the obstacle-weighting parameters within the EMPC cost function, enhancing both obstacle-avoidance and lane-keeping performance. Under multi-lane overtaking conditions, a lane-change trigger—implemented as a dual-phase “lane-change–return” mechanism—is employed, in which lateral optimization is activated only during critical phases, reducing online computational load by at least 28% compared with conventional MPC strategies. The experimental results demonstrate that the proposed PPO–EMPC architecture exhibits high robustness, real-time performance, and scalability under dynamic and partially observable environments, providing a practical and generalizable decision-making paradigm for cooperative AGV operations in automated container terminals. Full article

This research focuses on vehicle Advanced Driver Assistance Systems (ADAS), with particular emphasis on Lane Keeping Assist (LKA) systems which is designed to help drivers keep a vehicle centered within its lane and reduce the risk of unintentional lane departures. These kinds of systems detect lane boundaries using computer vision algorithms applied to video data captured by a forward-facing camera and interpret this visual information to provide corrective steering inputs or driver alerts. The research investigates the performance, reliability, sustainability, and limitations of LKA systems under adverse road and environmental conditions, such as wet pavement and in the presence of degraded, partially visible, or missing horizontal road markings. Improving the reliability of lane detection and keeping systems enhances road safety, reducing traffic accidents caused by lane departures, which directly supports social sustainability. For the theoretical test, a modified road model using MATLAB software was used to simulate poor road markings and to investigate possible test outcomes. A series of field tests were conducted on multiple passenger vehicles equipped with LKA technologies to evaluate their response in real-world scenarios. The results show that it is very important to ensure high quality horizontal road markings as specified in UNECE Regulation No. 130, as lane keeping aids are not uniformly effective. Furthermore, the study highlights the need to develop more robust line detection algorithms capable of adapting to diverse road and weather conditions, thereby enhancing overall driving safety and system reliability. LKA system research supports sustainable mobility strategies promoted by international organizations—aiming to transition to safer, smarter, and less polluting transportation systems. Full article

Driving in snowy conditions challenges both human drivers and autonomous systems. Snowfall and ice accumulation impair vehicle control and affect driver perception and performance. Road markings are often obscured, forcing drivers to rely on intuition and memory to stay in their lane, which can lead to encroachment into adjacent lanes or sidewalks. Current lane detectors assist in lane keeping, but their performance is compromised by visual disturbances such as ice reflection, snowflake movement, fog, and snow cover. Furthermore, testing these systems with users on actual snowy roads involves risks to driver safety, equipment integrity, and ethical compliance. This study presents a low-cost virtual reality simulation for evaluating winter lane detection in controlled and safe conditions from a human-in-the-loop perspective. Participants drove in a simulated snowy scenario with and without the detector while quantitative and qualitative variables were monitored. Results showed a 49.9% reduction in unintentional lane departures with the detector and significantly improved user experience, as measured by the UEQ-S (p = 0.023, Cohen’s d = 0.72). Participants also reported higher perceived safety, situational awareness, and confidence. These findings highlight the potential of vision-based lane detection systems adapted to winter environments and demonstrate the value of immersive simulations for user-centered testing of ADASs. Full article

Highway congestion is a persistent issue, and dynamically activating emergency lanes offers a promising mitigation strategy. However, traditional fixed-time or single-threshold methods often fail to balance traffic efficiency and safety. This paper introduces a dynamic optimization framework that integrates a Kriging surrogate model with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to identify optimal activation strategies. By simultaneously minimizing total travel time (efficiency) and the duration vehicles spend in unsafe proximity (safety), our method generates a set of Pareto-optimal solutions. We calibrated and validated the model using real-world highway data. The results are compelling: the optimized compromise strategy reduced total travel time by 20.5% compared to having no activation, while keeping safety risks within an acceptable range. The use of a Kriging surrogate model sped up the optimization process by approximately 20 times compared to direct simulation, achieving a prediction accuracy of 97.8%. The optimal strategies characteristically involve opening the emergency lane at the downstream bottleneck during peak congestion and closing it promptly as traffic eases. This research provides a robust, efficient, and practical decision-support tool for intelligent traffic management, offering a clear pathway to safer and less congested highways. Full article

Lane detection is an essential task in the field of computer vision and autonomous driving. This involves identifying and locating road markings on the road surface. This capability not only helps drivers keep the vehicle in the correct lane, but also provides critical data for advanced driver assistance systems and autonomous vehicles. Traditional lane detection models work mainly on the 2D image plane and achieve remarkable results. However, these models often assume a flat-world scenario, which does not correspond to real-world conditions, where roads have elevation variations and road markings may be curved. Our approach solves this challenge by focusing on 3D lane detection without relying on the inverse perspective mapping technique. Instead, we introduce a new framework using the MambaVision-S-1K backbone, which combines Mamba-based processing with Transformer capabilities to capture both local detail and global contexts from monocular images. This hybrid approach allows accurate modeling of lane geometry in three dimensions, even in the presence of elevation variations. By replacing the traditional convolutional neural network backbone with MambaVision, our proposed model significantly improves the capability of 3D lane detection systems. Our method achieved state-of-the-art performance on the ONCE-3DLanes dataset, thus demonstrating its superiority in accurately capturing lane curvature and elevation variations. These results highlight the potential of integrating advanced backbones based on Vision Transformers in the field of autonomous driving for more robust and reliable lane detection. The code will be available online. Full article

As automation technologies continue to advance within the automotive industry, urban road traffic is gradually shifting from conventional driving toward fully autonomous. This transition is supported by the progressive integration of partially automated functions, such as Adaptive Cruise Control (ACC) and lane-keeping assistance, which are already implemented in commercial vehicles and increasingly affect both individual driving behavior and overall traffic flow dynamics. The main purpose of this research is to evaluate the impact of automated vehicles presence in a complex signalized intersection under mixed traffic conditions, considering different penetration rates and demand levels. A review of previous modeling approaches from the literature was conducted, highlighting critical aspects to be considered in the design and simulation of road traffic. Field traffic data were collected and used as input for a microsimulation model developed in AIMSUN. A base scenario and a 20% growth scenario were analyzed to assess the impact of AV-ACC penetration, varying the AV-ACC’s rates in traffic composition. The results indicate that increased AV-ACC penetration rates, especially beyond 50%, contribute significantly to improving traffic stability and efficiency. Full article

Mind wandering (MW) is a significant safety risk in driving, yet research on its scope, underlying mechanisms, and mitigation strategies remains fragmented across disciplines. In this review guided by the PRISMA framework, we analyze findings from 64 empirical studies to address these factors. The presented study quantifies the prevalence of MW in naturalistic and simulated driving environments and shows its impact on driving behaviors. We document its negative effects on braking reaction times and lane-keeping consistency, and we assess recent advancements in objective detection methods, including EEG signatures, eye-tracking metrics, and physiological markers. We also identify key cognitive and contextual risk factors, including high perceived risk, route familiarity, and driver fatigue, which increase MW episodes. Also, we survey emergent countermeasures, such as haptic steering wheel alerts and adaptive cruise control perturbations, designed to sustain driver engagement. Despite these advancements, the MW research shows persistent challenges, including methodological heterogeneity that limits cross-study comparisons, a lack of real-world validation of detection algorithms, and a scarcity of long-term field trials of interventions. Our integrated synthesis, therefore, outlines a research agenda prioritizing harmonized measurement protocols, on-road algorithm deployment, and rigorous evaluation of countermeasures under naturalistic driving conditions. Full article

With the rapid adoption of automated driving systems, ensuring safe and efficient driver takeover has become a crucial challenge for road safety. This study introduces a novel psychological framework for understanding and predicting takeover behavior in conditionally automated vehicles, leveraging an extended Theory of Planned Behavior (TPB) model enriched by real-world driver experience. Drawing on survey data from 385 automated driving system users recruited in Shaoguan City, China, through face-to-face questionnaire administration covering various ADS types (ACC, lane-keeping, automatic parking), we demonstrate that driver attitudes, perceived behavioral control, and subjective norms are significant determinants of takeover intention, collectively explaining nearly half of its variance (R² = 48.7%). Importantly, our analysis uncovers that both intention and perceived behavioral control have robust, direct effects on actual takeover behavior. Crucially, this work is among the first to reveal that individual user characteristics—such as driving experience and ADS (automated driving system) usage frequency—substantially moderate these psychological pathways: experienced or frequent users rely more on perceived control and attitude, while less experienced drivers are more susceptible to social influences. By advancing a multi-dimensional psychological model that integrates personal, social, and experiential moderators, our findings deliver actionable insights for the design of adaptive human–machine interfaces, tailored driver training, and targeted safety interventions in the context of automated driving. Using structural equation modeling with maximum likelihood estimation (χ²/df = 2.25, CFI = 0.941, RMSEA = 0.057), this psychological approach complements traditional engineering models by revealing that takeover behavior variance is explained at 58.3%. Full article

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

The development of Advanced Driver Assistance Systems (ADASs) has reached a stage where, in addition to the traditional challenges of path planning and control, there is an increasing focus on the behavior of these systems. Assistance functions shall be personalized to deliver a full user experience. Therefore, driver modeling is a key area of research for next-generation ADASs. One of the most common tasks in everyday driving is lane keeping. Drivers are assisted by lane-keeping systems to keep their vehicle in the center of the lane. However, human drivers often deviate from the center line. It has been shown that the driver’s choice to deviate from the center line can be modeled by a linear combination of preview curvature information. This model is called the Linear Driver Model. In this paper, we fit the LDM parameters to real driving data. The drivers are then clustered based on the individual parameters. It is shown that clusters are not only formed by the numerical similarity of the driver parameters, but the drivers in a cluster actually have similar behavior in terms of path selection. Finally, an Extended Kalman Filter (EKF) is proposed to learn the model parameters at run-time. Any new driver can be classified into one of the driver type groups. This information can be used to modify the behavior of the lane-keeping system to mimic human driving, resulting in a more personalized driving experience. Full article

In recent years, with the rapid development of science and technology and the substantial improvement of computing power, various deep learning research topics have been promoted. However, existing autonomous driving technologies still face significant challenges in achieving robust lane-keeping and navigation performance, especially when transferring learned models from simulation to real-world environments due to environmental complexity and domain gaps. Many fields such as computer vision, natural language processing, and medical imaging have also accelerated their development due to the emergence of this wave, and the field of self-driving cars is no exception. The trend of self-driving cars is unstoppable. Many technology companies and automobile manufacturers have invested a lot of resources in the research and development of self-driving technology. With the emergence of different levels of self-driving cars, most car manufacturers have already reached the L2 level of self-driving classification standards and are moving towards L3 and L4 levels. This study applies deep reinforcement learning (DRL) to train autonomous vehicles with lane-keeping and navigation capabilities. Through simulation training and Sim2Real strategies, including domain randomization and CycleGAN, the trained models are evaluated in real-world environments to validate performance. The results demonstrate the feasibility of DRL-based autonomous driving and highlight the challenges in transferring models from simulation to reality. Full article

This study proposes a vision-based framework to support AVs in maintaining stable lane-keeping by assessing the condition of lane markings. Unlike existing infrastructure standards focused on human visibility, this study addresses the need for criteria suited to sensor-based AV environments. Using real driving data from urban expressways in Seoul, a YOLOv5-based lane detection algorithm was developed and enhanced through multi-label annotation and data augmentation. The model achieved a mean average precision (mAP) of 97.4% and demonstrated strong generalization on external datasets such as KITTI and TuSimple. For lane condition assessment, a pixel occupancy–based method was applied, combined with Canny edge detection and morphological operations. A threshold of 80-pixel occupancy was used to classify lanes as intact or worn. The proposed framework reliably detected lane degradation under various road and lighting conditions. These results suggest that quantitative, image-based indicators can complement traditional standards and guide AV-oriented infrastructure policy. Limitations include a lack of adverse weather data and dataset-specific threshold sensitivity. Full article

To enable autonomous driving in real-world environments that involve a diverse range of geographic variations and complex traffic regulations, it is essential to investigate Deep Reinforcement Learning (DRL) algorithms capable of policy learning in high-dimensional environments characterized by intricate state–action interactions. In particular, closed-loop experiments, which involve continuous interaction between an agent and their driving environment, serve as a critical framework for improving the practical applicability of DRL algorithms in autonomous driving systems. This study empirically analyzes the capabilities of several representative DRL algorithms—namely DDPG, SAC, TD3, PPO, TQC, and CrossQ—in handling various urban driving scenarios using the CARLA simulator within a closed-loop framework. To evaluate the adaptability of each algorithm to geographical variability and complex traffic laws, scenario-specific reward and penalty functions were carefully designed and incorporated. For a comprehensive performance assessment of the DRL algorithms, we defined several driving performance metrics, including Route Completion, Centerline Deviation Mean, Episode Reward Mean, and Success Rate, which collectively reflect the quality of the driving in terms of its completeness, stability, efficiency, and comfort. Experimental results demonstrate that TQC and SAC, both of which adopt off-policy learning and stochastic policies, achieve superior sample efficiency and learning performances. Notably, the presence of geographically variant features—such as traffic lights, intersections, and roundabouts—and their associated traffic rules within a given town pose significant challenges to driving performance, particularly in terms of Route Completion, Success Rate, and lane-keeping stability. In these challenging scenarios, the TQC algorithm achieved a Route Completion rate of 0.91, substantially outperforming the 0.23 rate observed with DDPG. This performance gap highlights the advantage of approaches like TQC and SAC, which address Q-value overestimation through statistical methods, in improving the robustness and effectiveness of autonomous driving in diverse urban environments. Full article

This paper introduces a robust yet straightforward lane detection and lateral control approach via the deployment of a dual camera based on the look-down strategy for autonomous vehicles. Unlike traditional single-camera systems that rely on the look-ahead methodology and a single front-facing preview, the proposed algorithm leverages two downward-facing cameras mounted beneath the vehicle’s driver and the passenger side mirror, respectively. This configuration captures the road surface, enabling precise detection of the lateral boundaries, particularly during lane changes and in narrow lanes. A Proportional-Integral-Derivative (PID) controller is designed to maintain the vehicle’s position in the center of the road. We compare this system’s accuracy, lateral steadiness, and computational efficiency against (1) a conventional bird’s-eye view lane detection method and (2) a popular deep learning-based lane detection framework. Experiments in the CARLA simulator under varying road geometries, lighting conditions, and lane marking qualities confirm that the proposed look-down system achieves superior real-time performance, comparable lane detection accuracy, and reduced computational overhead relative to both traditional bird’s-eye and advanced neural approaches. These findings underscore the practical benefits of a straightforward, explainable, and resource-efficient solution for robust autonomous vehicle lane-keeping. Full article