As automated vehicles (AVs) become increasingly popular, the question arises as to how cyclists will interact with such vehicles. This study investigated (1) whether cyclists spontaneously notice if a vehicle is driverless, (2) how well they perform a driver-detection task when explicitly instructed, and (3) how they carry out these tasks. Using a Wizard-of-Oz method, 37 participants cycled a designated route and encountered an AV multiple times in two experimental sessions. In Session 1, participants cycled the route uninstructed, while in Session 2, they were instructed to verbally report whether they detected the presence or absence of a driver. Additionally, we recorded participants’ gaze behaviour with eye-tracking and their responses in post-session interviews. The interviews revealed that 30% of the cyclists spontaneously mentioned the absence of a driver (Session 1), and when instructed (Session 2), they detected the absence and presence of the driver with 93% accuracy. The eye-tracking data showed that cyclists looked more frequently and for longer at the vehicle in Session 2 compared to Session 1. Additionally, participants exhibited intermittent sampling of the vehicle, and they looked at the area in front of the vehicle when it was far away and towards the windshield region when it was closer. The post-session interviews also indicated that participants were curious, but felt safe, and reported a need to receive information about the AV's driving state. In conclusion, cyclists can detect the absence of a driver in the AV, and this detection may influence their perception of safety. Further research is needed to explore these findings in real-world traffic conditions.

Recognizing and understanding implicit driving cues across diverse cultures is imperative for fostering safe and efficient global transportation systems, particularly when training new immigrants holding driving licenses from culturally disparate countries. Additionally, it is essential to consider cross-cultural differences in the development of Automated Driving features tailored to different countries. Previous piloting studies have compared and analyzed cross-cultural differences in selected implicit driving cues, but they typically examine only limited countries. However, a comprehensive worldwide comparison and analysis are lacking. This study conducts a thorough review of existing literature, online blogs, and expert insights from diverse countries to investigate cross-cultural disparities in driving behaviors, specifically focusing on implicit cues such as non-verbal communication (e.g., hand gestures, signal lighting, honking), norms, and social expectations. Through comparative analysis, variations in driving cues are illuminated across different cultural contexts. Based on the findings and identified gaps, a research roadmap is proposed for future research to further explore and address these differences, aiming to enhance intercultural communication, improve road safety, and increase transportation efficiency on a global scale. This paper presents the pioneering work towards a comprehensive understanding of the implicit driving cues across cultures. Moreover, this understanding will inform the development of automated driving systems tailored to different countries considering cross-cultural differences.

This paper investigates the motion control of automated vehicles for both lane-changing and lane-keeping maneuvers. This research is critical because lane keeping and lane changing, which need to be integrated into a unified control system, are still two fundamental control problems on the way to developing the highly automated vehicle. In addition, environment perception, which is highly coupled with motion control, should be introduced into the control loop. A further challenge is to solve the complex optimization problem with constraints of vehicle dynamics and full-dimensional collision avoidance. To solve these issues, this paper proposes a unified model predictive control method that can seamlessly handle lane-keeping and lane-changing maneuvers. The control problem adopts three reference generation approaches to get the perception of the traffic environment involved. Further, a rough-plan-and-fine-check strategy is utilized to reduce the complexity of solving the proposed unified model predictive control problem with constraints of collision avoidance. The proposed method has been implemented on the PreScan-MATLAB/Simulink joint simulation platform, where its performance of lane keeping and lane changing has been evaluated in different driving scenarios. Simulation results verify the capabilities of the proposed method.

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 (also referred to in this paper as “anomalies”). 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 machine (HELM)-based semi-supervised ML method using partly labeled data to accurately detect the identified abnormal driving behaviors. Moreover, previous ML-based approaches predominantly utilized basic vehicle motion features (such as velocity and acceleration) to label and detect abnormal driving behaviors, while this study seeks to introduce event-level safety indicators as 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 safety indicators serving as important features. The proposed semi-supervised ML method outperforms other baseline semi-supervised or unsupervised methods as far as various metrics are concerned: for example, it delivers the best accuracy at 99.58% and the best F1-score at 0.9913. The ablation study further highlights the significance of safety indicators for advancing the detection performance of abnormal driving behaviors.

Background
The steady development of automated vehicles (AVs) promises significant benefits in terms of traffic safety and efficiency. However, the transition to fully AVs and their deployment on the road will be gradual, leading to a phase of mixed-traffic conditions where AVs at various levels coexist with human-driven vehicles (HDVs). This transition poses unprecedented hurdles, requiring a deeper understanding of the emerging challenges for AVs in sensing and perceiving road environments, as well as in the novel interactions between AVs and HDVs. Furthermore, the social compliance of AVs and the optimization of their deployment strategies need to be considered as well.

Contents of this Thesis
This thesis addresses the multifaceted challenges associated with AVs’ development and deployment in mixed-traffic environments. The main objective of this thesis is to enhance the capabilities of AVs enabling them with a wider Operational Design Domain (ODD) and thus facilitate the implementation of safe, efficient, and socially compliant automated driving in mixed traffic. Referring to the modular design of AV systems, three key perspectives, i.e., sensing and perception, anomaly detection, as well as planning and control, are tackled in this thesis. To be specific:
Chapters 2-4 focus on enhancing sensing and perception capabilities through the development of hybrid spatial-temporal deep learning models and self-supervised pretraining methods. Lane detection is chosen as the focus of these chapters since it is vital for current vehicle localization and positioning, and it is also the foundation of various automated driving features. The main findings of these chapters are summarized as follows.
Chapter 2 presents a pioneering hybrid spatial-temporal sequence-to-one deep learning architecture tailored for vision-based lane detection tasks. By integrating the spatial convolutional neural network (SCNN) with spatial-temporal Recurrent Neural Network (RNN) modules, this architecture effectively captures correlations and dependencies among continuous image frames. Through extensive experimentation on various driving scenes, including challenging scenarios, the proposed model variants exhibit superior performance over existing state-of-the-art models. Notably, even the lighter model variants demonstrate remarkable accuracy, outperforming their counterparts while maintaining lower computational complexity.
Building upon the foundation laid in Chapter 2, Chapter 3 focuses on refining vision-based sensing and perception through the development of customized spatial-temporal attention mechanisms. These mechanisms, including temporal attention, spatial-temporal attention, and spatial-temporal attention with fully connected layers, are meticulously designed to optimize the utilization of spatial-temporal correlations across different regions of interest within the consecutive image frames. Leveraging linear Long Short Term Memory (LSTM) neural networks in conjunction with the proposed attention blocks, this chapter demonstrates the feasibility of lightweight and computationally efficient solutions for sequential deep neural networks (DNNs). Through rigorous experimentation, ablation studies, and comparative analysis across diverse datasets, the effectiveness of the proposed attention mechanisms in enhancing lane detection performance is convincingly established.
In Chapter 4, the exploration of enhancing vision-based sensing and perception capabilities continues with the introduction of a self-supervised pretraining method employing masked sequential autoencoders (MSAE). This innovative approach leverages both labelled and unlabelled data to improve detection accuracy and expedite the training process of DNN models dedicated to lane detection tasks. Additionally, a customized Focal Loss based PolyLoss is introduced to further enhance the detection accuracy. Through comprehensive experimentation and comparative analysis, the efficacy of the proposed pretraining method and loss function is demonstrated, showcasing substantial improvements in lane detection performance across diverse driving scenarios. Specifically, the utilization of MSAE-based pretraining and the adoption of the customized PolyLoss result in superior performance metrics, underscoring the pivotal role of self-supervised learning techniques and tailored loss functions in fortifying the robustness and efficiency of vision-based sensing and perception systems in AVs.
These chapters address the challenges of vision-based lane detection, crucial for AV navigation and safety.

Chapters 5-6 delve into anomaly detection, investigating techniques for identifying abnormal lane rendering in digital map applications and detecting anomalies in driving behaviour.
Chapter 5 introduces an innovative approach to anomaly detection in lane rendering images of digital map applications, utilizing Transformer-based models with self-supervised pretraining and customized fine-tuning. By transforming anomaly detection into a classification problem, the chapter proposes a four-phase pipeline that includes data pre-processing, self-supervised pre-training with masked image modelling (MiM), customized fine-tuning using cross-entropy-based loss, and post-processing. Experimental results demonstrate the pipeline’s effectiveness, with significant improvements in detection accuracy and reduced training time achieved through self-supervised pre-training. Ablation studies regarding tackling the problem with different numbers of classes further validate the pipeline’s performance enhancements, particularly in addressing data imbalance. This approach not only enhances anomaly detection accuracy but also contributes to reducing labour costs associated with manual labelling and anomaly detection efforts, offering significant societal benefits.
Additionally, Chapter 6 explores the critical task of detecting abnormal driving behaviour, addressing the need for more feasible and efficient approaches by leveraging semi-supervised ML methods. Utilizing large-scale real-world driving data, the study develops a semi-supervised ML model based on Hierarchical Extreme Learning Machines (HELM). This approach utilizes partly labelled data and introduces Surrogate Safety Measures (SSMs) (specifically the event-baed safety indicators of Two-Dimensional Time-To-Collision (2D-TTC)) as the pivotal input features to enhance performance. Results demonstrate the effectiveness of the proposed semi-supervised ML model, showcasing superior performance compared to baseline methods. The integration of SSMs significantly improves detection accuracy, highlighting their significant role in enhancing model performance. By leveraging unlabelled data for training and only a small sample of labelled data for fine-tuning, the proposed semi-supervised approach achieves competitive performance while reducing dependency on fully labelled datasets, making it suitable for real-world applications.
To sum up, the exploration of semi-supervised and self-supervised ML methods presents promising avenues in anomaly detection. The pioneering research presented in this thesis represents a significant stride towards leveraging data-driven ML-based anomaly detection methodologies to enhance the safety of driving.

Chapters 7-9 shift the focus to planning and control strategies for AVs, presenting a comprehensive examination of decision-making frameworks and control algorithms. These chapters introduce a conceptual framework aimed at fostering socially compliant driving behaviour and propose a range of model-based and learning-based approaches.
Chapter 7 lays the groundwork by introducing a conceptual framework that emphasizes socially compliant automated driving. This framework encompasses various social components such as cultural nuances, norms, and driving styles. A key innovation is the introduction of bidirectional behavioural adaptation, highlighting the dynamic interactions between AVs and human drivers. Furthermore, the framework advocates for the incorporation of a spatial-temporal memory module to enable continuous refinement of driving strategies, thereby promoting adaptability and safety in diverse traffic scenarios. Validation through an online expert survey lends credence to the framework’s efficacy. This conceptual framework lays a solid foundation for learning-based and model-based approaches for implementing planning and control algorithms for automated driving.
In the learning-based approach explored in Chapter 8, Deep Reinforcement Learning (DRL) takes centre stage, with a focus on integrating safety, efficiency, comfort level, and energy consumption considerations into the learning framework. Multiple DRL algorithms are evaluated across diverse driving manoeuvres, particularly roundabout driving, highlighting the importance of real-world requirements in reward function design and simulation-based training. Among the compared DRL algorithms, Trust Region Policy Optimization (TRPO) emerges as leading in safety and efficiency, while Proximal Policy Optimization (PPO) excels in comfort during roundabout driving. Moreover, the extension of the training environment to encompass various driving scenarios showcases the adaptability of DRL models to train a uniform driving model for real traffic environments, signalling promising avenues for future research.
Regarding the model-based approach, Chapter 9 introduces the DRF-SVO-MPCC algorithm, aimed at enhancing AVs’ understandability and predictability to human drivers, particularly during interactions with HDVs when driving through the roundabouts, as this challenging manoeuvre involves large curvature and tackles both longitudinal and lateral control. This algorithm integrates the perceived Driving Risk Field (DRF), Social Value Orientation (SVO), and Model Predictive Contouring Control (MPCC), enabling AVs to navigate social scenarios with sensitivity to the welfare of surrounding HDVs. Simulation experiments, conducted on various roundabout scenarios, underscore the algorithm’s superiority in trajectory tracking and adaptability to different driving styles, ensuring safety and social compliance. The findings illuminate the potential of the DRF-SVO-MPCC algorithm in fostering harmonious interactions between AVs and HDVs, setting a precedent for socially aware automated driving systems.

Overall, this thesis represents a solid endeavour to advance the planning and control capabilities of AVs in mixed-traffic environments. Through the development of novel conceptual frameworks and innovative model-based and learning-based algorithmic solutions, it lays the groundwork for the realization of safe, efficient, socially compliant, and adaptable automated driving, contributing to safer and more harmonious transportation systems.

Conclusion and perspectives
In summary, this thesis contributes to advancing the knowledge of how to improve automated driving systems in the realms of sensing and perception, anomaly detection, as well as planning and control. By integrating theoretical frameworks, methodological innovations, and data-driven empirical evaluations, notable progress has been achieved in fostering the development of safe, efficient, and socially compliant automated driving within mixed-traffic environments.
Despite the considerable progress made, several directions for future research have been identified. These include the imperative for more expansive high-quality datasets, exploration of domain adaptation techniques for both sensing and anomaly detection tasks, as well as the seamless integration of model-based and learning-based methodologies for planning and control. Additionally, transitioning towards a unified driving model and effectively addressing the complexities of multi-agent interactions in intricate urban settings remain pivotal areas for further exploration. Furthermore, interdisciplinary collaboration will be instrumental in harnessing the full potential of automated vehicles to revolutionize transportation systems.

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 mislead human drivers and consequently contribute to unsafe driving. In response to this concern 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: data preprocessing, self-supervised pretraining with the masked image modeling (MiM) method, customized fine-tuning using cross-entropy-based loss with label smoothing, and post-processing. Leveraging state-of-the-art deep learning techniques, especially those involving transformer models, the pipeline demonstrates superior performance verified through various experiments. Notably, self-supervised pretraining with MiM can greatly enhance detection accuracy while significantly reducing the total training time. For instance, employing the Swin Transformer with Uniform Masking as self-supervised pretraining yielded a higher accuracy of 94.77% and an improved area under the curve (AUC) score of 0.9743 compared with the pure Swin Transformer without pretraining with an accuracy of 94.01% and an AUC of 0.9498. Furthermore, fine-tuning epochs were dramatically reduced to 41 from the original 280. Ablation study with regard to techniques to alleviate the data imbalance between normal and abnormal instances further reinforces the model’s overall performance. In conclusion, the proposed pipeline, with its incorporation of self-supervised pretraining 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.

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.

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.

As automated vehicles (AVs) become increasingly popular, the question arises as to how cyclists will interact with such vehicles. This study investigated (1) whether cyclists spontaneously notice if a vehicle is driverless, (2) how well they perform a driver-detection task when explicitly instructed, and (3) how they carry out these tasks. Using a Wizard-of-Oz method, 37 participants cycled a designated route and encountered an AV multiple times in two experimental sessions. In Session 1, participants cycled the route uninstructed, while in Session 2, they were instructed to verbally report whether they detected the presence or absence of a driver. Additionally, we recorded participants' gaze behaviour with eye-tracking and their responses in post-session interviews. The interviews revealed that 30% of the cyclists spontaneously mentioned the absence of a driver (Session 1), and when instructed (Session 2), they detected the absence and presence of the driver with 93% accuracy. The eye-tracking data showed that cyclists looked more frequently and for longer at the vehicle in Session 2 compared to Session 1. Additionally, participants exhibited intermittent sampling of the vehicle, and they looked at the area in front of the vehicle when it was far away and towards the windshield region when it was closer. The post-session interviews also indicated that participants were curious, but felt safe, and reported a need to receive information about the AV's driving state. In conclusion, cyclists can detect the absence of a driver in the AV, and this detection may influence their perception of safety. Further research is needed to explore these findings in real-world traffic conditions.

Automated Vehicles (AVs) hold promise for revolutionizing transportation by improving road safety, traffic efficiency, and overall mobility. Despite the steady advancement in high-level AVs in recent years, the transition to full automation entails a period of mixed traffic, where AVs of varying automation levels coexist with human-driven vehicles (HDVs). Making AVs socially compliant and understood by human drivers is expected to improve the safety and efficiency of mixed traffic. Thus, ensuring AVs compatibility with HDVs and social acceptance is crucial for their successful and seamless integration into mixed traffic. However, research in this critical area of developing Socially Compliant AVs (SCAVs) remains sparse. This study carries out the first comprehensive scoping review to assess the current state of the art in developing SCAVs, identifying key concepts, methodological approaches, and research gaps. An expert interview was also conducted to identify critical research gaps and expectations towards SCAVs. Based on the scoping review and expert interview input, a conceptual framework is proposed for the development of SCAVs. The conceptual framework is evaluated using an online survey targeting researchers, technicians, policymakers, and other relevant professionals worldwide. The survey results provide valuable validation and insights, affirming the significance of the proposed conceptual framework in tackling the challenges of integrating AVs into mixed-traffic environments. Additionally, future research perspectives and suggestions are discussed, contributing to the research and development agenda of SCAVs.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.