Complex Traffic Environments Research Articles

Pedestrian safety alarm system based on binocular distance measurement for trucks using recognition feature analysis

AbstractAs an essential part of modern smart manufacturing, road transport with large and heavy trucks has in-creased dramatically. Due to the inside wheel difference in the process of turning, there is a considerable safety hazard in the blind area of the inside wheel difference. In this paper, multiple cameras combined with deep learning algorithms are introduced to detect pedestrians in the blind area of wheel error. A scheme of vehicle-pedestrian safety alarm detection system is developed via the integration of YOLOv5 and an improved binocular distance measurement method. The system accurately measures the distance between the truck and nearby pedestrians by utilizing multiple cameras and PP Human recognition, providing real-time safety alerts. The experimental results show that this method significantly reduces distance measurement errors, improves the reliability of pedestrian detection, achieves high accuracy and real-time performance, and thus enhances the safety of trucks in complex traffic environments.

Vehicle trajectory extraction with interacting multiple model for low-channel roadside LiDAR

High-precision and consistent vehicle trajectories encompass microscopic traffic parameters, mesoscopic traffic flow characteristics, and macroscopic traffic flow features, which is the cornerstone of innovation in data-driven traffic management and control applications. However, occlusion and trajectory interruption remain challenging in multivehicle tracking under complex traffic environments using low-channel roadside LiDAR. To address the challenge, a novel framework for vehicle trajectory extraction using low-channel roadside LiDAR was proposed. First, the geometric features of the cluster and its L-shape bounding box were used to address the over-segmentation in vehicle detection arising from occlusion and point cloud sparse. Then, objects within adjacent point cloud frames were associated by developing an improved Hungarian algorithm integrated with an adaptive distance threshold to solve the mismatching problem caused by objects entrancing and exiting in a new point cloud frame. Finally, an improved interacting multiple model by considering vehicle driving patterns was deployed to predict the location of missing vehicles and connect the interrupted trajectories. Experimental results showed that the proposed methods achieve 98.76 % of vehicle detection accuracy and 97.40 % of data association precision. The mean absolute error (MAE) and mean square error (MSE) of the vehicle position estimation are 0.2252 m and 0.0729 m2, respectively. The trajectory extraction precision outperforms most of the state-of-the-art algorithms.

Risk-Aware Lane Change and Trajectory Planning for Connected Autonomous Vehicles Based on a Potential Field Model

To enhance the safety of lane changes for connected autonomous vehicles in an intelligent transportation environment, this study draws from potential field theory to analyze variations in the risks that vehicles face under different traffic conditions. The safe minimum vehicle distance is dynamically adjusted, and a comprehensive vehicle risk potential field model is developed. This model systematically quantifies the risks encountered by connected autonomous vehicles during the driving process, providing a more accurate assessment of safety conditions. Subsequently, vehicle motion is decoupled into lateral and longitudinal components within the Frenet coordinate system, with quintic polynomials employed to generate clusters of potential trajectories. To improve computational efficiency, trajectory evaluation metrics are developed based on vehicle dynamics, incorporating factors such as acceleration, jerk, and curvature. An initial filtering process is applied to these trajectories, yielding a refined set of candidates. These candidate trajectories are further assessed using a minimum safety distance model derived from potential field theory, with optimization focusing on safety, comfort, and efficiency. The algorithm is tested in a three-lane curved simulation environment that includes both constant-speed and variable-speed lane change scenarios. Results show that the collision risk between the target vehicle and surrounding vehicles remains below the minimum safety distance threshold throughout the lane change process, ensuring a high level of safety. Furthermore, across various driving conditions, the target vehicle’s acceleration, jerk, and trajectory curvature remained well within acceptable limits, demonstrating that the proposed lane change trajectory planning algorithm successfully balances safety, comfort, and smoothness, even in complex traffic environments.

Safedrive dreamer: Navigating safety–critical scenarios in autonomous driving with world models

Achieving stable and reliable autonomous driving in complex traffic environments while ensuring safety under unpredictable conditions is a critical challenge in autonomous driving technology. To address this issue, this study proposes the Safedrive Dreamer navigation framework, which aims to reduce the reliance on trial-and-error learning in real-world scenarios, thereby mitigating the risks associated with dynamic driving conditions and enhancing vehicle foresight. This framework integrates the predictive capabilities of world models with the constrained Markov decision process (CMDP) and safety reinforcement learning to accurately anticipate future environmental changes. This ensures the reliability of autonomous driving routes, thereby improving both safety and efficiency. Furthermore, to reduce trial-and-error costs in real-world applications, this study employs PAC-Bayesian methods to derive generalization error bounds between simulations and reality, enabling a more effective transfer of knowledge and experience from simulations to real-world scenarios. Validation experiments in simulated and real environments showed that Safedrive Dreamer significantly outperformed existing autonomous driving solutions by 3.8% in key safety metrics, excelling in collision avoidance and risk reduction. This study provides new insights into the integration of world models into decision-making processes to enhance decision-making capabilities in safety–critical applications, thereby contributing significantly to the improvement of autonomous driving safety and reliability.

Road object detection method based on improved YOLOv5 algorithm

Aiming at the existing network's poor recognition of distant targets in road traffic scenes, insufficient expression of target features, and inaccurate target positioning, a road target detection method based on the improved YOLOv5 algorithm is proposed. Firstly, the feature extraction structure of the YOLOv5 algorithm is summarized, and the shortcomings of the original network structure are analyzed; secondly, a small target detection layer is added on the basis of the original network, and the recognition ability of the network for distant targets is improved by supplementing the fusion feature layer and introducing an additional detection head; thirdly, the original detection head is decoupled, and the expression ability of the network for target features is improved by changing the bounding box regression and target classification process into two branches; then, the prior frame is re-clustered, and the height-width ratio of the prior frame is adjusted by the K-means++ algorithm to enhance the network's positioning ability for the target; finally, ablation, comparison and visualization verification experiments are carried out with AP, mAP and FPS as evaluation indicators. The results show that the detection speed of the proposed algorithm on the BDD100K dataset is 95.2 frames/second, and the average accuracy reaches 55.6%, which is higher than that of the YOLOv5 algorithm 6.7%. It can be seen that the improved YOLOv5 algorithm has good target detection accuracy while meeting the real-time requirements of detection. It is suitable for road target detection tasks in complex traffic environments and has guiding significance for improving the visual perception ability of autonomous vehicles.

Hybrid lane change strategy of autonomous vehicles based on SOAR cognitive architecture and deep reinforcement learning

Research on lane change strategies for autonomous vehicles holds paramount importance in optimizing traffic flow efficiency, enhancing driving safety, and adapting to complex traffic environments. While numerous rule-based or machine-learning approaches have been explored to tackle the challenge of lane change on highways, they frequently exhibit limited performance owing to the complexity of driving environments. This study proposes a novel lane change strategy for autonomous vehicles, which utilizes a hybrid framework integrating the SOAR cognitive architecture and deep reinforcement learning (DRL) to address the lane change challenge on highways. First, we introduce a rule extraction algorithm, the RuleCOSI+, which is based on tree ensemble algorithms, designed to extract concise lane change rules from large-scale human driving data. These straightforward rules, together with traffic regulations and safety rules, constitute the long-term memory of the SOAR cognitive architecture, enabling transparent decision-making processes. Next, by analyzing the clipping mechanism of the proximal policy optimization (PPO) algorithm, we propose an Adaptive Clipping PPO (ACPPO) algorithm which is based on the importance of samples. This algorithm adopts different clipping strategies for SOAR samples and ACPPO samples during the training process, enabling the algorithm to more effectively utilize samples with different levels of importance. Then, we propose a hybrid decision-making algorithm: SOAR-ACPPO, which combines the SOAR cognitive architecture with the ACPPO algorithm. This algorithm leverages SOAR’s prior knowledge to effectively and safely guide agent learning. Finally, by selecting appropriate intervention probability and weaning strategy, the system avoids inappropriate knowledge intervention and ensures adequate environment exploration. Simulation experiments conducted using the CARLA simulator illustrate that the proposed strategy not only improves model learning efficiency but also enhances driving efficiency and safety. Additionally, it demonstrates a certain degree of human-like characteristics and interpretability.

PV-LaP: Multi-sensor fusion for 3D Scene Understanding in intelligent transportation systems

Intelligent transportation systems are pivotal in modern urban development, aiming to enhance traffic management efficiency, safety, and sustainability. However, existing 3D Visual Scene Understanding methods often face challenges of robustness and high computational complexity in complex traffic environments. This paper proposes a Multi-Sensor Signal Fusion method based on PV-RCNN and LapDepth (PV-LaP) to improve 3D Visual Scene Understanding. By integrating camera and LiDAR data, the PV-LaP method enhances environmental perception accuracy. Evaluated on the KITTI and WHU-TLS datasets, the PV-LaP framework demonstrated superior performance. On the KITTI dataset, our method achieved an Absolute Relative Error (Abs Rel) of 0.079 and a Root Mean Squared Error (RMSE) of 3.014, outperforming state-of-the-art methods. On the WHU-TLS dataset, the method improved 3D reconstruction precision with a PSNR of 19.15 and an LPIPS of 0.299. Despite its high computational demands, PV-LaP offers significant improvements in accuracy and robustness, providing valuable insights for the future development of intelligent transportation systems.

RLCFormer: Automatic roadside LiDAR-Camera calibration framework with transformer

LiDAR-Camera fusion is pivotal for perceiving and understanding complex traffic environments, particularly valuable in autonomous driving and traffic monitoring. Traditional calibration algorithms, primarily designed for onboard sensors, are inadequate for roadside setups where sensors are positioned higher and more dispersed. To address this challenge, we introduce the RLCFormer, a Transformer-based network specifically tailored for precise calibration of roadside sensors. This method innovatively integrates depth and RGB features, utilizing correlation layers and a Transformer decoder to accurately match features across modalities. Evaluated on the DAIR-V2X-I Roadside 3D detection dataset, the RLCFormer achieves an average translation error of 3.3187 cm and a rotation error of 0.0469°, surpassing existing methods. Our approach significantly enhances scene representation and calibration precision, offering a robust solution for roadside sensor calibration and advancing the state of the art in sensor fusion technology.

Road surface semantic segmentation for autonomous driving

Although semantic segmentation is widely employed in autonomous driving, its performance in segmenting road surfaces falls short in complex traffic environments. This study proposes a frequency-based semantic segmentation with a transformer (FSSFormer) based on the sensitivity of semantic segmentation to frequency information. Specifically, we propose a weight-sharing factorized attention to select important frequency features that can improve the segmentation performance of overlapping targets. Moreover, to address boundary information loss, we used a cross-attention method combining spatial and frequency features to obtain further detailed pixel information. To improve the segmentation accuracy in complex road scenarios, we adopted a parallel-gated feedforward network segmentation method to encode the position information. Extensive experiments demonstrate that the mIoU of FSSFormer increased by 2% compared with existing segmentation methods on the Cityscapes dataset.

Deep learning in driverless: Research results, issues, challenges

As computing and communication technologies continue to evolve, leading technology companies are investing significant resources in self-driving car techniques. With the use of deep learning in driverless driving, driverless cars are enabling them to sense, make decisions and control in complex environments, leading to truly autonomous driving. Deep learning is used for environment perception, which enables driverless cars to understand their surroundings by recognizing information such as road surfaces, pedestrians and vehicles, and also used for decision formulation, by predicting the behavior of other vehicles, pedestrians and identifying the environment around the road conditions to control the driverless car to make the optimal driving decision. But in order to achieve fully autonomous driving, there are still some technical aspects. Therefore, we deep learning in the existing driverless technology is reviewed, mainly discussing the application in the direction of driverless implementation and safety inspection. Through reading a large amount of literature, we discuss the problems that hinder the development of the driverless field, and at the same time, how to solve the problems in the future to promote the breakthrough of the driverless field. The application of deep learning in driverless not only enhances the intelligence level of driverless cars, but also prepares for the commercial application of driverless cars in the future. It enables driverless cars to better identify and adapt to complex traffic environments. Finally, realizing truly autonomous driving.

Enhancing YOLO for occluded vehicle detection with grouped orthogonal attention and dense object repulsion

In real-life complex traffic environments, vehicles are often occluded by extraneous background objects and other vehicles, leading to severe degradation of object detector performance. To address this issue, we propose a method named YOLO-OVD (YOLO for occluded vehicle detection) and a dataset for effectively handling vehicle occlusion in various scenarios. To highlight the model attention in unobstructed region of vehicles, we design a novel grouped orthogonal attention (GOA) module to achieve maximum information extraction between channels. We utilize grouping and channel shuffling to address the initialization and computational issues of original orthogonal filters, followed by spatial attention for enhancing spatial features in vehicle-visible regions. We introduce a CIoU-based repulsion term into the loss function to augment the network’s localization accuracy in scenarios involving densely packed vehicles. Moreover, we explore the effect of the knowledge-based Laplacian Pyramid on the OVD performance, which contributes to fast convergence in training and ensures more detailed and comprehensive feature retention. We conduct extensive experiments on the established occluded vehicle detection dataset, which demonstrates that the proposed YOLO-OVD model significantly outperforms 14 representative object detectors. Notably, it achieves improvements of 4.7% in Precision, 3.6% in AP@0.5, and 1.9% in AP@0.5:0.95 compared to the YOLOv5 baseline.

Research on traffic sign detection algorithm based on improved SSD in complex environments

Abstract In complex traffic sign environments, detection challenges include uneven size distribution, insufficient valid information, and difficulties in identifying targets under resource constraints, leading to missed and false detections. This study proposes an enhanced lightweight traffic sign detection algorithm based on SSD (Single Shot MultiBox Detector). By replacing the original backbone network with MobileNetV2, the model is streamlined to have fewer parameters, which improves generalization in complex environments. This modification significantly boosts the recall rate, achieving a better balance between precision and recall. By introducing the FPN(Feature Pyramid Network) combined with the CBAM(Convolutional Block Attention Module) attention mechanism, the detailed and semantic information between deep and shallow layers is fully integrated, reducing the loss of feature information, thus enhancing the strengthening of key information of traffic signs and the adaptability to different scales of traffic signs. Finally, by integrating the cross-attention mechanism, the algorithm's anti-interference ability in complex environments is improved, and the positioning accuracy of traffic signs is enhanced by capturing the dependency between different positions. Through ablation experiments and comparative experiments on a public traffic sign dataset, our improved SSD algorithm achieved an mAP of 89.97%. Compared with the original algorithm, the mAP increased by 12.41%, the recall rate increased by 18.38%, and the sum of precision and recall F1 increased by 14.6%. These improvements significantly enhance the performance of traffic sign detection in complex environments, thereby meeting the performance requirements of traffic sign detection.

Constraint-Guided Behavior Transformer for Centralized Coordination of Connected and Automated Vehicles at Intersections.

The centralized coordination of Connected and Automated Vehicles (CAVs) at unsignalized intersections aims to enhance traffic efficiency, driving safety, and passenger comfort. Autonomous Intersection Management (AIM) systems introduce a novel approach for centralized coordination. However, existing rule-based and optimization methods often face the challenges of poor generalization and low computational efficiency when dealing with complex traffic environments and highly dynamic traffic conditions. Additionally, current Reinforcement Learning (RL)-based methods encounter difficulties around policy inference and safety. To address these issues, this study proposes Constraint-Guided Behavior Transformer for Safe Reinforcement Learning (CoBT-SRL), which uses transformers as the policy network to achieve efficient decision-making for vehicle driving behaviors. This method leverages the ability of transformers to capture long-range dependencies and improve data sample efficiency by using historical states, actions, and reward and cost returns to predict future actions. Furthermore, to enhance policy exploration performance, a sequence-level entropy regularizer is introduced to encourage policy exploration while ensuring the safety of policy updates. Simulation results indicate that CoBT-SRL exhibits stable training progress and converges effectively. CoBT-SRL outperforms other RL methods and vehicle intersection coordination schemes (VICS) based on optimal control in terms of traffic efficiency, driving safety, and passenger comfort.

CycleInSight: An enhanced YOLO approach for vulnerable cyclist detection in urban environments

As urbanization continues to reshape transportation, the safety of cyclists in complex traffic environments has become a pressing concern. In response to this challenge, our research introduces a CycleInSight framework, which harnesses advanced deep learning and computer vision techniques to enable precise and efficient cyclist detection in diverse urban settings. Utilizing you only look once version 8 (YOLOv8) object detection algorithm, the proposed model aims to detect and localize vulnerable cyclists near vehicles equipped with onboard cameras. Our research presents comprehensive experimental results demonstrating its effectiveness in identifying vulnerable cyclists amidst dynamic and challenging traffic conditions. With an impressive average precision of 90.91%, our approach outperforms existing models while maintaining efficient inference speeds. By effectively identifying and tracking cyclists, this framework holds significant potential to enhance urban traffic safety, inform data-driven infrastructure planning, and support the development of advanced driver assistance systems and autonomous vehicles.

Vehicle Behavior Discovery and Three-Dimensional Object Detection and Tracking Based on Spatio-Temporal Dependency Knowledge and Artificial Fish Swarm Algorithm.

In complex traffic environments, 3D target tracking and detection are often occluded by various stationary and moving objects. When the target is occluded, its apparent characteristics change, resulting in a decrease in the accuracy of tracking and detection. In order to solve this problem, we propose to learn the vehicle behavior from the driving data, predict and calibrate the vehicle trajectory, and finally use the artificial fish swarm algorithm to optimize the tracking results. The experiments show that compared with the CenterTrack method, the proposed method improves the key indicators of MOTA (Multi-Object Tracking Accuracy) in 3D object detection and tracking on the nuScenes dataset, and the frame rate is 26 fps.

TSD‐YOLO: Small traffic sign detection based on improved YOLO v8

AbstractTraffic sign detection is critical for autonomous driving technology. However, accurately detecting traffic signs in complex traffic environments remains challenge despite the widespread use of one‐stage detection algorithms known for their real‐time processing capabilities. In this paper, the authors propose a traffic sign detection method based on YOLO v8. Specifically, this study introduces the Space‐to‐Depth (SPD) module to address missed detections caused by multi‐scale variations of traffic signs in traffic scenes. The SPD module compresses spatial information into depth channels, expanding the receptive field and enhancing the detection capabilities for objects of varying sizes. Furthermore, to address missed detections caused by complex backgrounds such as trees, this paper employs the Select Kernel attention mechanism. This mechanism enables the model to dynamically adjust its focus and more effectively concentrate on key features. Additionally, considering the uneven distribution of training data, the authors adopted the WIoUv3 loss function, which optimizes loss calculation through a weighted approach, thereby improving the model's detection performance across various sizes and frequencies of instances. The proposed methods were validated on the CCTSDB and TT100K datasets. Experimental results demonstrate that the authors’ method achieves substantial improvements of 3.2% and 5.1% on the mAP50 metric compared to YOLOv8s, while maintaining high detection speed, significantly enhancing the overall performance of the detection system. The code for this paper is located at https://github.com/dusongjie/TSD‐YOLO‐Small‐Traffic‐Sign‐Detection‐Based‐on‐Improved‐YOLO‐v8

Dynamic Perception-Based Vehicle Trajectory Prediction Using a Memory-Enhanced Spatio-Temporal Graph Network

In the realm of intelligent transportation systems, accurately predicting vehicle trajectories is paramount for enhancing road safety and optimizing traffic flow management. Addressing the impacts of complex traffic environments and efficiently modeling the diverse behaviors of vehicles are the key challenges at present. To achieve precise prediction of vehicle trajectories, it is essential to fully consider the dynamic changes in traffic conditions and the long-term dependencies of time-series data. In response to these challenges, we propose the Memory-Enhanced Spatio-Temporal Graph Network (MESTGN), an innovative model that integrates a Spatio-Temporal Graph Convolutional Network (STGCN) with an attention-enhanced Long Short-Term Memory (LSTM)-based sequence to sequence (Seq2Seq) encoder–decoder structure. MESTGN utilizes STGCN to capture the complex spatial dependencies between vehicles and reflects the interactions within the traffic network through road traffic data and network topology, which significantly influences trajectory prediction. Additionally, the model focuses on historical vehicle trajectory data points using an attention-weighted mechanism under a traditional LSTM prediction architecture, calculating the importance of critical trajectory points. Finally, our experiments conducted on the urban traffic dataset ApolloSpace validate the effectiveness of our proposed model. We demonstrate that MESTGN shows a significant performance improvement in vehicle trajectory prediction compared with existing mainstream models, thereby confirming its increased prediction accuracy.

Safe Autonomy: Sensor Fusion in Complex Traffic Environments

Advancements in autopilot driving and car technology have propelled the development of safe autonomy, particularly in navigating complex traffic environments. This paper delves into applying sensor fusion techniques in automobile systems, focusing on their relevance in ensuring safe navigation amidst the intricacies of Indian road conditions. Of course, the conditions of Indian roads have changed hugely and drastically over time but still, it would not be wrong to say that India is still grappling with a high incidence of road accidents exacerbated by non-compliance with traffic rules, integrating sensor fusion technologies becomes pivotal in enhancing the safety and reliability of autonomous systems. By including and adding data from various sensors such as cameras, LiDAR, and radar, these systems gain a comprehensive understanding of their surroundings, enabling real-time decision-making in dynamic traffic scenarios. Furthermore, the paper explores the integration of machine learning algorithms to augment sensor fusion capabilities, facilitating adaptive responses to erratic traffic behaviour and irregular driving practices. Through a technology-centric lens, this research aims to play the role of sensor fusion in bolstering the efficacy of autonomous systems on Indian roads, thereby contributing to the mitigation of road accidents and promoting safer transportation infrastructures.

An Adaptive Route Guidance Model Considering the Effect of Traffic Signals Based on Deep Reinforcement Learning

Navigation or route guidance systems are designed to provide drivers with real-time travel information and the associated recommended routes for their trips. Classical route choice models typically rely on utility theory to represent drivers’ route choice behavior. Such choices, however, may not be optimal from both the individual and the system perspectives. This is simply due to the fact that drivers usually have imperfect knowledge about the time-varying traffic conditions. In this article, we explore and propose a new model-free deep reinforcement learning (DRL) approach to solving the adaptive route guidance problem based on microsimulation. The proposed approach consists of three interconnected algorithms, including a network edge labeling algorithm, a routing plan identification algorithm, and an adaptive route guidance algorithm. Simulation experiments on both a toy network and a real-world network of Suzhou, China, are performed to demonstrate the effectiveness of the proposed approach in terms of guiding a single vehicle as well as multiple vehicles through complex traffic environments. Comparative results confirm that the DRL approach outperforms the traditional shortest path method by further reducing the average travel time in the network.

Research on the vehicle lane-changing decision-making system in complex traffic environments

In order to ensure the safety of vehicles changing lanes, there should be a certain degree of interaction and dynamics between vehicles on the way. In this paper, the problems of non-interactive road information and imprecise path planning are investigated. Through the research of the road condition decision-making system, autonomous lane-changing assistance system and intelligent traffic system, vehicle wireless networking technology, vehicle-road cooperation technology, and lane-changing decision-making and planning technology are used to increase the frequency of information interaction between vehicles and the external environment in order to realize information interaction between vehicles and collaborative driving. In this paper, simulation experiments of vehicle lane-changing are carried out by using MATLAB Simulink and compared with other research models. The results show that the lane change planning data based on the road condition decision system is more accurate, which leads to fast lane change driving in complex traffic scenarios.