Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, economic benefits. Although number of surveys have reviewed research achievements this field, they are still limited specific tasks, lack systematic summary directions future. Here we propose Survey Surveys (SoS) for total technologies AD IVs that reviews history, summarizes milestones, provides perspectives, ethics, future directions. To our knowledge, article first...

Considering that human-driven vehicles and autonomous (AVs) will coexist on roads in the future for a long time, how to merge AVs into human drivers' traffic ecology minimize effect of their misfit with drivers, are issues worthy consideration. Moreover, different passengers have needs AVs, thus, provide personalized choices is another issue AVs. Therefore, human-like decision making framework designed this paper. Different driving styles social interaction characteristics formulated...

The diagnosis of the key components rotating machinery systems is essential for production efficiency and quality manufacturing processes. performance traditional method depends heavily on feature extraction, which relies degree individual's expertise or prior knowledge. Recently, a deep learning (DL) applied to automate extraction. However, training in DL requires massive amount sensor data, time consuming poses challenge its applications engineering. In this paper, new data-driven fault...

Reinforcementlearning holds the promise of allowing autonomous vehicles to learn complex decision making behaviors through interacting with other traffic participants. However, many real-world driving tasks involve unpredictable perception errors or measurement noises which may mislead an vehicle into unsafe decisions, even cause catastrophic failures. In light these risks, ensure safety under uncertainty, are required be able cope worst case observation perturbations. Therefore, this paper...

Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving transportation systems to digitize synergize connected automated vehicles. However, existing studies focus on the design of vehicle, whereas digitization human driver, who plays important role in driving, largely ignored. Furthermore, previous driver-related tasks are limited specific scenarios have applicability. Thus, a novel concept driver digital twin (DDT) proposed this study bridge gap between...

Due to its limited intelligence and abilities, machine learning is currently unable handle various situations thus cannot completely replace humans in real-world applications. Because exhibit robustness adaptability complex scenarios, it crucial introduce into the training loop of artificial (AI), leveraging human further advance algorithms. In this study, a real-time human-guidance-based (Hug)-deep reinforcement (DRL) method developed for policy an end-to-end autonomous driving case. With...

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, economic benefits. Although number of surveys have reviewed research achievements this field, they are still limited specific tasks lack systematic summaries directions future. Our work divided into three independent articles first part survey (SoS) for total technologies AD IVs that involves history, summarizes milestones, provides perspectives, ethics, future...

This paper presents a novel integrated approach to deal with the decision making and motion planning for lane-change maneuvers of autonomous vehicle (AV) considering social behaviors surrounding traffic occupants. Reflected by driving styles intentions vehicles, are taken into consideration during modelling process. Then, Stackelberg Game theory is applied solve decision-making, which formulated as non-cooperative game problem. Besides, potential field adopted in model, uses different...

The diagnosis of complicated fault severity problems in rotating machinery systems is an important issue that affects the productivity and quality manufacturing processes industrial applications. However, it usually suffers from several deficiencies. (1) A considerable degree prior knowledge expertise required to not only extract select specific features raw sensor signals, but also choose a suitable fusion for information. (2) Traditional artificial neural networks with shallow...

To address the safety and efficiency issues of vehicles at multi-lane merging zones, a cooperative decision-making framework is designed for connected automated (CAVs) using coalitional game approach. Firstly, motion prediction module established based on simplified single-track vehicle model enhancing accuracy reliability algorithm. Then, cost function constraints decision making are considering multiple performance indexes, i.e. safety, comfort efficiency. Besides, in order to realize...

Driver attention estimation is one of the key technologies for intelligent vehicles. The existing related methods only focus on scene image or driver's gaze head pose. purpose this article to propose a more reasonable and feasible method based dual-view with calibration-free direction. According human visual mechanisms, low-level features, static saliency map, dynamic optical flow information are extracted as input feature maps, which combine high-level semantic descriptions probability map...

To improve the safety and efficiency of intelligent transportation system, particularly in complex urban scenarios, this paper a game theoretic decision-making framework is designed for connected automated vehicles (CAVs) at unsignalized roundabouts considering their personalized driving behaviours. Within framework, motion prediction module optimized using model predictive control (MPC) to enhance effectiveness accuracy algorithm. Besides, payoff function decision making defined with...

Connected automated driving technologies have shown tremendous improvement in recent years. However, it is still not clear how behaviors and energy consumption correlate with each other to what extent these factors related connected vehicles can influence the motion prediction performance. The precise recognition of vehicle critical safety for (CAVs). Hence, this study, an energy-aware pattern analysis system are proposed CAVs using a deep learning-based time-series modeling approach. First,...

Driver anomaly quantification is a fundamental capability to support human-centric driving systems of intelligent vehicles. Existing studies usually treat it as classification task and obtain discrete levels for abnormalities. Meanwhile, the existing data-driven approaches depend on quality dataset provide limited recognition unknown activities. To overcome these challenges, this paper proposes contrastive learning approach with aim building model that can quantify driver anomalies...

Accurate dynamic driver head pose tracking is of great importance for driver–automotive collaboration, intelligent copilot, head-up display (HUD), and other human-centered automated driving applications. To further advance this technology, article proposes a low-cost markerless head-tracking system using deep learning-based estimation model. The proposed requires only red, green, blue (RGB) camera without hardware or markers. enhance the accuracy driver’s estimation, spatiotemporal vision...

Considering personalized driving preferences, a new decision-making framework is developed using differential game approach to resolve the conflicts of autonomous vehicles (AVs) at unsignalized intersections. To realize human-like and decision-making, aggressiveness first defined for AVs. improve safety, Gaussian potential field model built collision risk assessment. Besides, in proposed framework, assessment further used reduce computational complexity based on an event-triggered mechanism....

Driver workload inference is significant for the design of intelligent human–machine cooperative driving schemes since it allows systems to alert drivers before potentially dangerous maneuvers and achieve a safer control transition. However, pattern variations among individual sensor artifacts pose great challenges existing cognitive recognition approaches. In this article, we develop an attention-enabled network with decision-level fusion architecture further improve estimation performance....

Ensuring safety and achieving human-level driving performance remain challenges for autonomous vehicles, especially in safety-critical situations. As a key component of artificial intelligence, reinforcement learning is promising has shown great potential many complex tasks; however, its lack guarantees limits real-world applicability. Hence, further advancing learning, from the perspective, importance driving. revealed by cognitive neuroscientists, amygdala brain can elicit defensive...

Accurate recognition of driver distraction is significant for the design human-machine cooperation driving systems. Existing studies mainly focus on classifying varied distracted behaviors, which depend heavily scale and quality datasets only detect discrete categories. Therefore, most data-driven approaches have limited capability recognizing unseen activities cannot provide a reasonable solution downstream applications. To address these challenges, this paper develops vision...

Driver drowsiness detection is of great significance in improving driving safety and has been widely studied recent years. However, some existing methods have not fully utilized the drowsiness-related information, are susceptible to interference from redundant information input data. To address these issues, a video-based driver method according key facial features including landmarks local areas (VBFLLFA) proposed this paper. In order utilize related exclude head movement obtained through...

In this article, a human–machine adaptive shared control method is proposed for automated vehicles (AVs) under automation performance degradation. First, novel risk assessment module to monitor driving behavior and evaluate degradation AVs. Then, an authority allocation developed. the event of any degradation, allocated system decreased based on assessed risk. Consequently, human driver adaptively increased thus requires more engagement in loop compensate ensure vehicle's safety....

To address the coordination issue of connected automated vehicles (CAVs) at urban scenarios, a game-theoretic decision-making framework is proposed that can advance social benefits, including traffic system efficiency and safety, as well benefits individual users. Under framework, in this work, representative driving scenario, i.e. unsignalized intersection, investigated. Once vehicle enters focused zone, it will interact with other CAVs make collaborative decisions. evaluate safety risk...