Automated vehicles (AVs) may require the implementation of an external human-machine interface (eHMI) to communicate their intentions to human-driven vehicles. The optimal placement of the eHMI, either on the AV itself or as part of the road infrastructure, remains undetermined. The current driving simulator study investigated the effect of eHMI positioning on human driving behaviour, during the approach and execution of right turns at T-intersections. Forty-three participants drove under three conditions: absence of eHMI, eHMI on the AV (eHMIv), and eHMI integrated into the infrastructure (eHMIi). Participants encountered AVs that either yielded or did not yield to their vehicles. The results regarding the placement of the eHMI showed that both concepts are advantageous, but for different reasons. eHMIv was appreciated because implicit and explicit communication are congruent, although the AV must first be visually identified to respond to it. eHMIi was appreciated because a familiar cue is always at a known location in the environment; as a result, participants braked earlier for the intersection and came less close to the AV (which can be interpreted as a safety advantage or an efficiency disadvantage). Although there are limitations to the current driving simulator study, this research provides important insights into the fundamental question of how information placement affects drivers’ visual attention demands and driving behaviour, topics that are important in view of the development of future cities.

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The burgeoning navigation services using digital maps provide great convenience to drivers. However, there are sometimes anomalies in the lane rendering map images, which might mislead human drivers and result in unsafe driving. To accurately and effectively detect the anomalies, this paper transforms lane rendering image anomaly detection into a classification problem and proposes a four-phase pipeline consisting of data pre-processing, self-supervised pre-training with the masked image modeling (MiM) method, customized fine-tuning using cross-entropy loss with label smoothing, and post-processing to tackle it using state-of-the-art deep learning techniques, especially the Transformer models. Various experiments verify the effectiveness of the proposed pipeline. The proposed pipeline can deliver superior lane rendering image anomaly detection performance, and especially, the self-supervised pre-training with MiM can greatly improve the detection accuracy while significantly reducing the total training time, e.g, Swin Transformer with Uniform Masking as self-supervised pretraining (Swin-Trans-UM) obtained better accuracy at 94.77% and better Area Under The Curve (AUC) at 0.9743 compared with the pure Swin Transformer without pre-training (Swin-Trans) whose accuracy is 94.01% AUC is 0.9498, and the fine-tuning epochs reduced to 41 from original 280. Ablation study further regarding techniques to alleviate the data imbalance between normal and abnormal instances further enhances the model performance. @en

Medical waste recycling and treatment has gradually drawn concerns from the whole society, as the amount of medical waste generated is increasing dramatically, especially during the pandemic of COVID-19. To tackle the emerging challenges, this study designs a reverse logistics system architecture with three modules, i.e., medical waste classification & monitoring module, temporary storage & disposal site (disposal site for short) selection module, as well as route optimization module. This overall solution design won the Grand Prize of the “YUNFENG CUP” China National Contest on Green Supply and Reverse Logistics Design ranking 1 st . This paper focuses on the design of the route optimization module. In this module, a route optimization problem is designed considering transportation costs and multiple risk costs (e.g., environment risk, population risk, property risk, and other accident-related risks). The Analytic Hierarchy Process is employed to determine the weights for each risk element, and a customized genetic algorithm is developed to solve the route optimization problem. A case study under the COVID-19 pandemic is further provided to verify the proposed model. Limited by length, detailed descriptions of the whole system and the other modules can be found at https://shorturl.at/cdY59.@en

Developing and testing automated driving models in the real world might be challenging and even dangerous, while simulation can help with this, especially for challenging maneuvers. Deep reinforcement learning (DRL) has the potential to tackle complex decision-making and controlling tasks through learning and interacting with the environment, thus it is suitable for developing automated driving while not being explored in detail yet. This study carried out a comprehensive study by implementing, evaluating, and comparing the two DRL algorithms, Deep Q-networks (DQN) and Trust Region Policy Optimization (TRPO), for training automated driving on the highway-env simulation platform. Effective and customized reward functions were developed and the implemented algorithms were evaluated in terms of onlane accuracy (how well the car drives on the road within the lane), efficiency (how fast the car drives), safety (how likely the car is to crash into obstacles), and comfort (how much the car makes jerks, e.g., suddenly accelerates or brakes). Results show that the TRPO-based models with modified reward functions delivered the best performance in most cases. Furthermore, to train a uniform driving model that can tackle various driving maneuvers besides the specific ones, this study expanded the highway-env and developed an extra customized training environment, namely, ComplexRoads, integrating various driving maneuvers and multiple road scenarios together. Models trained on the designed ComplexRoads environment can adapt well to other driving maneuvers with promising overall performance. Lastly, several functionalities were added to the highway-env to implement this work. The codes are open on GitHub at https://github.com/alaineman/drlcarsim-paper.@en

The platform carrying capacity of urban rail transit stations is limited and overcrowding of the platform will lead to serious safety risks for passengers and trains. It is significant to collaborate on the optimization of passenger flow strategy and skip-stopping scheme to alleviate traffic pressure and ensure platform safety. This study proposes and solves the joint optimization problem of coordinated flow control and skip-stopping scheme considering platform carrying capacity. Firstly, platform demand constraints and platform stranded constraints are designed according to the maximum carrying capacity of the platform to control the number of allowable arrivals ensuring platform safety. Secondly, train arrival variable and train stop variable are introduced to generate train skip-stopping index. Finally, considering the characteristics of passengers' continuous arrival and platform carrying capacity, a mixed integer programming model is established to minimize the number of passengers outside the station and the number of passengers stranded on the platform. Based on empirical data, this study takes Beijing Batong line as a case study and uses the established model to generate flow control strategy and skip-stopping schemes for each station during morning rush hours. Experimental results show that compared to the baseline without implementing the two proposed strategies, the proposed collaborative optimization method can effectively reduce the demand for staying at the platform and increase the number of boarding passengers at downstream stations. Thus, the balance between train capacity and passenger flow demand is maintained while ensuring platform safety. Moreover, the proposed method can also avoid overcrowding at downstream stations.@en

Condition-based maintenance is becoming increasingly important in hydraulic systems. However, anomaly detection for these systems remains challenging, especially since that anomalous data is scarce and labeling such data is tedious and even dangerous. Therefore, it is advisable to make use of unsupervised or semi-supervised methods, especially for semi-supervised learning which utilizes unsupervised learning as a feature extraction mechanism to aid the supervised part when only a small number of labels are available. This study systematically compares semi-supervised learning methods applied for anomaly detection in hydraulic condition monitoring systems. Firstly, thorough data analysis and feature learning were carried out to understand the open-sourced hydraulic condition monitoring dataset. Then, various methods were implemented and evaluated including traditional standalone semi-supervised learning models (e.g., one-class SVM, Robust Covariance), ensemble models (e.g., Isolation Forest), and deep neural network based models (e.g., autoencoder, Hierarchical Extreme Learning Machine (HELM)). Typically, this study customized and implemented an extreme learning machine based semi-supervised HELM model and verified its superiority over other semi-supervised methods. Extensive experiments show that the customized HELM model obtained state-of-the-art performance with the highest accuracy (99.5%), the lowest false positive rate (0.015), and the best F1-score (0.985) beating other semi-supervised methods.@en

Detecting abnormal driving behavior is critical for road traffic safety and the evaluation of drivers' behavior. With the advancement of machine learning (ML) algorithms and the accumulation of naturalistic driving data, many ML models have been adopted for abnormal driving behavior detection. Most existing ML-based detectors rely on (fully) supervised ML methods, which require substantial labeled data. However, ground truth labels are not always available in the real world, and labeling large amounts of data is tedious. Thus, there is a need to explore unsupervised or semi-supervised methods to make the anomaly detection process more feasible and efficient. To fill this research gap, this study analyzes large-scale real-world data revealing several abnormal driving behaviors (e.g., sudden acceleration, rapid lane-changing) and develops a Hierarchical Extreme Learning Machines (HELM) based semi-supervised ML method using partly labeled data to accurately detect the identified abnormal driving behaviors. Moreover, previous ML-based approaches predominantly utilize basic vehicle motion features (such as velocity and acceleration) to label and detect abnormal driving behaviors, while this study seeks to introduce Surrogate Safety Measures (SSMs) as the input features for ML models to improve the detection performance. Results from extensive experiments demonstrate the effectiveness of the proposed semi-supervised ML model with the introduced SSMs serving as important features. The proposed semi-supervised ML method outperforms other baseline semi-supervised or unsupervised methods regarding various metrics, e.g., delivering the best accuracy at 99.58% and the best F-1 measure at 0.9913. The ablation study further highlights the significance of SSMs for advancing detection performance. @en

Traffic scenarios in roundabouts pose substantial complexity for automated driving. Manually mapping all possible scenarios into a state space is labor-intensive and challenging. Deep reinforcement learning (DRL) with its ability to learn from interacting with the environment emerges as a promising solution for training such automated driving models. This study explores, employs, and implements various DRL algorithms, namely Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO), and Trust Region Policy Optimization (TRPO) to instruct automated vehicles' driving through roundabouts. The driving state space, action space, and reward function are designed. The reward function considers safety, efficiency, comfort, and energy consumption to align with real-world requirements. All three tested DRL algorithms succeed in enabling automated vehicles to drive through the roundabout. To holistically evaluate the performance of these algorithms, this study establishes an evaluation methodology considering multiple indicators, i.e., safety, efficiency, comfort and energy consumption level. A method employing the Analytic Hierarchy Process is also developed to weigh these evaluation indicators. Experimental results on various testing scenarios reveal that the TRPO algorithm outperforms DDPG and PPO in terms of safety and efficiency, while PPO performs the best in terms of comfort level and energy consumption. Lastly, to verify the model's adaptability and robustness regarding other driving scenarios, this study also deploys the model trained by TRPO to a range of different testing scenarios, e.g., highway driving and merging. Experimental results demonstrate that the TRPO model trained on only roundabout driving scenarios exhibits a certain degree of proficiency in highway driving and merging scenarios. This study provides a foundation for the application of automated driving with DRL.@en

The gradual deployment of automated vehicles (AVs) results in mixed traffic where AVs will interact with human-driven vehicles (HDVs). Thus, social-aware motion planning and control while considering interactions with HDVs on the road is critical for AVs' deployment and safe driving under various maneuvers. Previous research mostly focuses on the trajectory planning of AVs using Model Predictive Control or other relevant methods, while seldom considering the integrated planning and control of AVs altogether to simplify the whole pipeline architecture. Furthermore, there are very limited studies on social-aware driving that makes AVs understandable and expected by human drivers, and none when it comes to the challenging maneuver of driving through roundabouts. To fill these research gaps, this paper develops an integrated social-aware planning and control algorithm for AVs' driving through roundabouts based on Driving Risk Field (DRF), Social Value Orientation (SVO), and Model Predictive Contouring Control (MPCC), i.e., DRF-SVO-MPCC. The proposed method is tested and verified with simulation on the open-sourced highway-env platform. Compared with the baseline method using purely Nonlinear Model Predictive Control, the DRF-SVO-MPCC can achieve better performance under various maneuvers of driving through roundabouts with and without surrounding HDVs.@en

The gradual deployment of automated vehicles (AVs) results in mixed traffic where AVs will interact with human-driven vehicles (HDVs). Thus, social-aware motion planning and control while considering interactions with HDVs on the road is critical for AVs’ deployment and safe driving under various maneuvers. Previous research mostly focuses on the trajectory planning of AVs using Model Predictive Control or other relevant methods, while seldom considering the integrated planning and control of AVs altogether to simplify the whole pipeline architecture. Furthermore, there are very limited studies on social-aware driving that makes AVs understandable and expected by human drivers, and none when it comes to the challenging maneuver of driving through roundabouts. To fill these research gaps, this paper develops an integrated social-aware planning and control algorithm for AVs’ driving through roundabouts based on Driving Risk Field (DRF), Social Value Orientation (SVO), and Model Predictive Contouring Control (MPCC), i.e., DRF-SVO-MPCC. The proposed method is tested and verified with simulation on the open-sourced highway-env platform. Compared with the baseline method using purely Nonlinear Model Predictive Control, the DRF-SVO-MPCC can achieve better performance under various maneuvers of driving through roundabouts with and without surrounding HDVs.@en

Lane detection is crucial for vehicle localization which makes it the foundation for automated driving and many intelligent and advanced driving assistant systems. Available vision-based lane detection methods do not make full use of the valuable features and aggregate contextual information, especially the interrelationships between lane lines and other regions of the images in continuous frames. To fill this research gap and upgrade lane detection performance, this paper proposes a pipeline consisting of self pre-training with masked sequential autoencoders and fine-tuning with customized PolyLoss for the end-to-end neural network models using multi-continuous image frames. The masked sequential autoencoders are adopted to pretrain the neural network models with reconstructing the missing pixels from a random masked image as the objective. Then, in the fine-tuning segmentation phase where lane detection segmentation is performed, the continuous image frames are served as the inputs, and the pre-trained model weights are transferred and further updated using the backpropagation mechanism with customized PolyLoss calculating the weighted errors between the output lane detection results and the labeled ground truth. Extensive experiment results demonstrate that, with the proposed pipeline, the lane detection model performance on both normal and challenging scenes can be advanced beyond the state-of-the art results, while the training time can be substantially shortened.@en

Detecting abnormal driving behavior is critical for road traffic safety and the evaluation of drivers' behavior. With the advancement of machine learning (ML) algorithms and the accumulation of naturalistic driving data, many ML models have been adopted for abnormal driving behavior detection. Most existing ML-based detectors rely on (fully) supervised ML methods, which require substantial labeled data. However, ground truth labels are not always available in the real world, and labeling large amounts of data is tedious. Thus, there is a need to explore unsupervised or semi-supervised methods to make the anomaly detection process more feasible and efficient. To fill this research gap, this study analyzes large-scale real-world data revealing several abnormal driving behaviors (e.g., sudden acceleration, rapid lane-changing) and develops a Hierarchical Extreme Learning Machines (HELM) based semi-supervised ML method using partly labeled data to accurately detect the identified abnormal driving behaviors. Moreover, previous ML-based approaches predominantly utilize basic vehicle motion features (such as velocity and acceleration) to label and detect abnormal driving behaviors, while this study seeks to introduce Surrogate Safety Measures (SSMs) as the input features for ML models to improve the detection performance. Results from extensive experiments demonstrate the effectiveness of the proposed semi-supervised ML model with the introduced SSMs serving as important features. The proposed semi-supervised ML method outperforms other baseline semi-supervised or unsupervised methods regarding various metrics, e.g., delivering the best accuracy at 99.58% and the best F-1 measure at 0.9913. The ablation study further highlights the significance of SSMs for advancing detection performance.@en

With social progress and the development of modern medical technology, the amount of medical waste generated is increasing dramatically. The problem of medical waste recycling and treatment has gradually drawn concerns from the whole society. The sudden outbreak of the COVID-19 epidemic further brought new challenges. To tackle the challenges, this study proposes a reverse logistics system architecture with three modules, i.e., medical waste classification & monitoring module, temporary storage & disposal site (disposal site for short) selection module, as well as route optimization module. This overall solution design won the Grand Prize of the “YUNFENG CUP” China National Contest on Green Supply and Reverse Logistics Design ranking 1 st . This paper focuses on the description of architectural design and the module on site selection. Specifically, regarding system architecture, a framework diagram is provided, together with brief descriptions of the three proposed modules and a case study under the COVID-19 epidemic with the customized model. Regarding the disposal site selection module, a multi-objective optimization model is developed, and considering different types of waste collection sites (i.e., prioritized large collection sites and common collection sites), a hierarchical solution method is developed employing linear programming and K-means clustering algorithms sequentially. The proposed site selection method is verified with a case study using real-world data, and compared with the baseline, it can immensely reduce the daily operational costs and working time. Limited by length, detailed descriptions of the whole system as well as the remaining medical waste classification & monitoring module and route optimization module can be found at https://shorturl.at/cdY59.
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Lane detection is crucial for vehicle localization which makes it the foundation for automated driving and many intelligent and advanced driving assistant systems. Available vision-based lane detection methods do not make full use of the valuable features and aggregate contextual information, especially the interrelationships between lane lines and other regions of the images in continuous frames. To fill this research gap and upgrade lane detection performance, this paper proposes a pipeline consisting of self pre-training with masked sequential autoencoders and fine-tuning with customized PolyLoss for the end-to-end neural network models using multi-continuous image frames. The masked sequential autoencoders are adopted to pre-train the neural network models with reconstructing the missing pixels from a random masked image as the objective. Then, in the fine-tuning segmentation phase where lane detection segmentation is performed, the continuous image frames are served as the inputs, and the pre-trained model weights are transferred and further updated using the backpropagation mechanism with customized PolyLoss calculating the weighted errors between the output lane detection results and the labeled ground truth. Extensive experiment results demonstrate that, with the proposed pipeline, the lane detection model performance on both normal and challenging scenes can be advanced beyond the state-of-the-art, delivering the best testing accuracy (98.38%), precision (0.937), and F1-measure (0.924) on the normal scene testing set, together with the best overall accuracy (98.36%) and precision (0.844) in the challenging scene test set, while the training time can be substantially shortened.

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Lane detection serves as a fundamental task for automated vehicles and Advanced Driver Assistance Systems. However, current lane detection methods can not deliver the versatility of accurate, robust, and realtime compatible lane detection in real-world scenarios especially under challenging driving scenes. Available vision-based methods in the literature do not consider critical regions of the image and their spatial-temporal salience regarding the detection results, thus they deliver poor performance in peculiar difficult circumstances (e.g., serious occlusion, dazzle lighting). This study aims to introduce a novel sequential neural network model with a spatial-temporal attention mechanism that can focus on key features of lane lines and exploit salient spatial-temporal correlations among continuous image frames for the purpose of enhancing the accuracy and robustness of lane detection. Under the regular encoder-decoder structure and with the implementation using common neural network backbones, the proposed model is trained and evaluated on three large-scale opensource datasets. Extensive experiments demonstrate the strength and the robustness of the proposed model outperforming available state-of-the-art methods in various testing.@en

The burgeoning navigation services using digital maps provide great convenience to drivers. Nevertheless, the presence of anomalies in lane rendering map images occasionally introduces potential hazards, as such anomalies can be misleading to human drivers and consequently contribute to unsafe driving conditions. In response to this concern and to accurately and effectively detect the anomalies, this paper transforms lane rendering image anomaly detection into a classification problem and proposes a four-phase pipeline consisting of data pre-processing, self-supervised pre-training with the masked image modeling (MiM) method, customized fine-tuning using cross-entropy based loss with label smoothing, and post-processing to tackle it leveraging state-of-the-art deep learning techniques, especially those involving Transformer models. Various experiments verify the effectiveness of the proposed pipeline. Results indicate that the proposed pipeline exhibits superior performance in lane rendering image anomaly detection, and notably, the self-supervised pre-training with MiM can greatly enhance the detection accuracy while significantly reducing the total training time. For instance, employing the Swin Transformer with Uniform Masking as self-supervised pretraining (Swin-Trans-UM) yielded a heightened accuracy at 94.77% and an improved Area Under The Curve (AUC) score of 0.9743 compared with the pure Swin Transformer without pre-training (Swin-Trans) with an accuracy of 94.01% and an AUC of 0.9498. The fine-tuning epochs were dramatically reduced to 41 from the original 280. In conclusion, the proposed pipeline, with its incorporation of self-supervised pre-training using MiM and other advanced deep learning techniques, emerges as a robust solution for enhancing the accuracy and efficiency of lane rendering image anomaly detection in digital navigation systems.@en

As the central nerve of the intelligent vehicle control system, the in-vehicle network bus is crucial to the security of vehicle driving. One of the best standards for the in-vehicle network is the Controller Area Network (CAN bus) protocol. However, the CAN bus is designed to be vulnerable to various attacks due to its lack of security mechanisms. To enhance the security of in-vehicle networks and promote the research in this area, based upon a large scale of CAN network traffic data with the extracted valuable features, this study comprehensively compared fully-supervised machine learning with semi-supervised machine learning methods for CAN message anomaly detection. Both traditional machine learning models (including single classifier and ensemble models) and neural network based deep learning models are evaluated. Furthermore, this study proposed a deep autoencoder based semi-supervised learning method applied for CAN message anomaly detection and verified its superiority over other semi-supervised methods. Extensive experiments show that the fully-supervised methods generally outperform semi-supervised ones as they are using more information as inputs. Typically the developed XGBoost based model obtained state-of-the-art performance with the best accuracy (98.65%), precision (0.9853), and ROC AUC (0.9585) beating other methods reported in the literature.@en

Automated driving systems, which can take over certain dynamic driving tasks from the driver, are becoming increasingly available in commercial vehicles. One of these automated driving systems widely introduced in commercial vehicles is Adaptive Cruise Control (ACC). This system is designed to maintain certain desired driving speeds and time headways as chosen by drivers and based on the settings available within the system. The properties and actual performance of these systems will affect the traffic flow and its stability. However, the specific properties and their workings are rarely publicly available. Therefore, the main aim of this paper is to test the actual performance of a commercial ACC system under different desired speed and distance gap settings, as well as driving modes in a car-following situation. For this purpose, a pilot field test was conducted in the Netherlands in which two identical commercial vehicles equipped with ACC systems were driven simultaneously. The first vehicle was used to create a prespecified speed profile by adapting the ACC system settings manually, while the second vehicle followed the lead vehicle when the ACC system was engaged to test its actual performance. The main findings indicate that the different system settings affect the car-following indicators, and system response times were found to be comparable to human response times. The eco mode was found to affect some of the car-following indicators, and it does not deteriorate safety below the safety level of driving with short headway setting in drive mode.@en

The gradual deployment of automated vehicles on the existing road network will lead to a long transition period in which vehicles at different driving automation levels and capabilities will share the road with human driven vehicles, resulting into what is known as mixed traffic. Whether our road infrastructure is ready to safely and efficiently accommodate this mixed traffic remains a knowledge gap. Microscopic traffic simulation provides a proactive approach for assessing these implications. However, differences in assumptions regarding modeling automated driving in current simulation studies, and the use of different terminology make it difficult to compare the results of these studies. Therefore, the aim of this study is to specify the aspects to consider for modeling automated driving in microscopic traffic simulations using harmonized concepts, to investigate how both empirical studies and microscopic traffic simulation studies on automated driving have considered the proposed aspects, and to identify the state of the practice and the research needs to further improve the modeling of automated driving. Six important aspects were identified: the role of authorities, the role of users, the vehicle system, the perception of surroundings based on the vehicle’s sensors, the vehicle connectivity features, and the role of the infrastructure both physical and digital. The research gaps and research directions in relation to these aspects are identified and proposed, these might bring great benefits for the development of more accurate and realistic modeling of automated driving in microscopic traffic simulations. @en

Accurate and reliable lane detection is vital for the safe performance of lane-keeping assistance and lane departure warning systems. However, under certain challenging circumstances, it is difficult to get satisfactory performance in accurately detecting the lanes from one single image as mostly done in current literature. Since lane markings are continuous lines, the lanes that are difficult to be accurately detected in the current single image can potentially be better deduced if information from previous frames is incorporated. This study proposes a novel hybrid spatial–temporal (ST) sequence-to-one deep learning architecture. This architecture makes full use of the ST information in multiple continuous image frames to detect the lane markings in the very last frame. Specifically, the hybrid model integrates the following aspects: (a) the single image feature extraction module equipped with the spatial convolutional neural network; (b) the ST feature integration module constructed by ST recurrent neural network; (c) the encoder–decoder structure, which makes this image segmentation problem work in an end-to-end supervised learning format. Extensive experiments reveal that the proposed model architecture can effectively handle challenging driving scenes and outperforms available state-of-the-art methods.

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