Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems

Clustering is an unsupervised learning technology,and it groups information(observations or datasets)according to similarity measures.Developing clustering algorithms is a hot topic in recent years,and this area develops rapidly with the increasing complexity of data and the volume of datasets.In this paper,the concept of clustering is introduced,and the clustering technologies are analyzed from traditional and modern perspectives.First,this paper summarizes the principles,advantages,and disadvantages of 20 traditional clustering algorithms and 4 modern algorithms.Then,the core elements of clustering are presented,such as similarity measures and evaluation index.Considering that data processing is often applied in vehicle engineering,finally,some specific applications of clustering algorithms in vehicles are listed and the future development of clustering in the era of big data is highlighted.The purpose of this review is to make a comprehensive survey that helps readers learn various clustering algorithms and choose the appropriate methods to use,especially in vehicles.

ML-ANet:A Transfer Learning Approach Using Adaptation Network for Multi-label Image Classification in Autonomous Driving

To reduce the discrepancy between the source and target domains,a new multi-label adaptation network(ML-ANet)based on multiple kernel variants with maximum mean discrepancies is proposed in this paper.The hidden representations of the task-specific layers in ML-ANet are embedded in the reproducing kernel Hilbert space(RKHS)so that the mean-embeddings of specific features in different domains could be precisely matched.Multiple kernel functions are used to improve feature distribution efficiency for explicit mean embedding matching,which can further reduce domain discrepancy.Adverse weather and cross-camera adaptation examinations are conducted to verify the effectiveness of our proposed ML-ANet.The results show that our proposed ML-ANet achieves higher accuracies than the compared state-of-the-art methods for multi-label image classification in both the adverse weather adaptation and cross-camera adaptation experiments.These results indicate that ML-ANet can alleviate the reliance on fully labeled training data and improve the accuracy of multi-label image classification in various domain shift scenarios.

On‑Ramp Merging for Highway Autonomous Driving:An Application of a New Safety Indicator in Deep Reinforcement Learning

This paper proposes an improved decision-making method based on deep reinforcement learning to address on-ramp merging challenges in highway autonomous driving.A novel safety indicator,time difference to merging(TDTM),is introduced,which is used in conjunction with the classic time to collision(TTC)indicator to evaluate driving safety and assist the merging vehicle in finding a suitable gap in traffic,thereby enhancing driving safety.The training of an autonomous driving agent is performed using the Deep Deterministic Policy Gradient(DDPG)algorithm.An action-masking mechanism is deployed to prevent unsafe actions during the policy exploration phase.The proposed DDPG+TDTM+TTC solution is tested in on-ramp merging scenarios with different driving speeds in SUMO and achieves a success rate of 99.96%without significantly impacting traffic efficiency on the main road.The results demonstrate that DDPG+TDTM+TTC achieved a higher on-ramp merging success rate of 99.96%compared to DDPG+TTC and DDPG.

Preface for Human‑Like Smart Autonomous Driving for Intelligent Vehicles and Transportation Systems

Drivers are the center of vehicles and transportation systems.Because of the rapid development of advanced technologies,artificial drivers have been developed as key elements in vehicles and transportation systems.The inconsistency between human drivers and artificial drivers will lead to accidents and congestion.To make future vehicles and transportation systems trustworthy in driving safety and acceptable in travel efficiency,developing technologies based on human drivers’reliable knowledge and cognitive intelligence together with smart operations is an essential and promising solution.However,there are many challenges to be addressed including the learning of smart human perception,reliable smart inference strategies in decision-making,adaptive correction of inappropriate driving operation,knowledge mapping and enhancement of smart human driving in various scenarios,etc.

Review and Perspectives on Human Emotion for Connected Automated Vehicles

The progression toward automated driving and the latest advancement in vehicular networking have led to novel and natural human-vehicle-road systems,in which affective human-vehicle interaction is a crucial factor affecting the acceptance,safety,comfort,and traffic efficiency of connected and automated vehicles(CAVs).This development has inspired increasing inter-est in how to develop affective interaction framework for intelligent cockpit in CAVs.To enable affective human-vehicle interactions in CAVs,knowledge from multiple research areas is needed,including automotive engineering,transportation engineering,human-machine interaction,computer science,communication,as well as industrial engineering.However,there is currently no systematic survey considering the close relationship between human-vehicle-road and human emotion in the human-vehicle-road coupling process in the CAV context.To facilitate progress in this area,this paper provides a comprehensive literature survey on emotion-related studies from multi-aspects for better design of affective interaction in intelligent cockpit for CAVs.This paper discusses the multimodal expression of human emotions,investigates the human emotion experiment in driving,and particularly emphasizes previous knowledge on human emotion detection,regulation,as well as their applications in CAVs.The promising research perspectives are outlined for researchers and engineers from different research areas to develop CAVs with better acceptance,safety,comfort,and enjoyment for users.

Depth Estimation Based on Monocular Camera Sensors in Autonomous Vehicles: A Self‑supervised Learning Approach

Estimating depth from images captured by camera sensors is crucial for the advancement of autonomous driving technologies and has gained significant attention in recent years.However,most previous methods rely on stacked pooling or stride convolution to extract high-level features,which can limit network performance and lead to information redundancy.This paper proposes an improved bidirectional feature pyramid module(BiFPN)and a channel attention module(Seblock:squeeze and excitation)to address these issues in existing methods based on monocular camera sensor.The Seblock redistributes channel feature weights to enhance useful information,while the improved BiFPN facilitates efficient fusion of multi-scale features.The proposed method is in an end-to-end solution without any additional post-processing,resulting in efficient depth estimation.Experiment results show that the proposed method is competitive with state-of-the-art algorithms and preserves fine-grained texture of scene depth.

Drivers’ EEG Responses to Different Distraction Tasks

Driver distraction has been deemed a major cause of traffic accidents.However,drivers’brain response activities to different distraction types have not been well investigated.The purpose of this study is to investigate the response of electroencephalography(EEG)activities to different distraction tasks.In the conducted simulation tests,three secondary tasks(i.e.,a clock task,a 2-back task,and a navigation task)are designed to induce different types of driver distractions.Twenty-four participants are recruited for the designed tests,and differences in drivers’brain response activities concerning distraction types are investigated.The results show that the differences in comprehensive distraction are more significant than that in single cognitive distraction.Friedman test and post hoc two-tailed Nemenyi test are conducted to further identify the differences in band activities among brain regions.The results show that the theta energy in the frontal lobe is significantly higher than that in other brain regions in distracted driving,whereas the alpha energy in the temporal lobe significantly decreases compared to other brain regions.These results provide theoretical references for the development of distraction detection systems based on EEG signals.

Study of Longitudinal-Vertical Dynamics for In-Wheel Motor-Driven Electric Vehicles

The in-wheel motor(IWM)-driven electric vehicles(EVs)attract increasing attention due to their advantages in dimensions and controllability.The majority of the current studies on IWM are carried out with the assumption of an ideal actuator,in which the coupling effects between the non-ideal IWM and vehicle are ignored.This paper uses the braking process as an example to investigate the longitudinal-vertical dynamics of IWM-driven EVs while considering the mechanical-electrical coupling effect.First,a nonlinear switched reluctance motor model is developed,and the unbalanced electric magnetic force(UEMF)induced by static and dynamic mixed eccentricity is analyzed.Then,the UEMF is decomposed into longitudinal and vertical directions and included in the longitudinal-vertical vehicle dynamics.The coupling dynamics are demonstrated under different vehicle braking scenarios;numerical simulations are carried out for various road grades,road friction,and vehicle velocities.A novel dynamics vibration absorbing system is adopted to improve the vehicle dynamics.Finally,the simulation results show that vehicle vertical dynamic performance is enhanced.

Deep Reinforcement Learning Enabled Decision-Making for Autonomous Driving at Intersections

Road intersection is one of the most complex and accident-prone traffic scenarios,so it’s challenging for autonomous vehicles(AVs)to make safe and efficient decisions at the intersections.Most of the related studies focus on the solution to a single scenario or only guarantee safety without considering driving efficiency.To address these problems,this study proposed a deep reinforcement learning enabled decision-making framework for AVs to drive through intersections automatically,safely and efficiently.The mapping relationship between traffic images and vehicle operations was obtained by an end-to-end decision-making framework established by convolutional neural networks.Traffic images collected at two timesteps were used to calculate the relative velocity between vehicles.Markov decision process was employed to model the interaction between AVs and other vehicles,and the deep Q-network algorithm was utilized to obtain the optimal driving policy regarding safety and efficiency.To verify the effectiveness of the proposed decision-making framework,the top three accident-prone crossing path crash scenarios at intersections were simulated,when different initial vehicle states were adopted for better generalization capability.The results showed that the developed method could make AVs drive safely and efficiently through intersections in all of the tested scenarios.

Detection of coalbed fractures with P-wave azimuthal AVO in 3-D seismic exploration

The detection of fractures is important for production and safety in coal fields. Subsurface fractures result in azimuthal anisotropy of the seismic wave, and the amplitude of reflection wave varies with offset and azimuth. In case of weak anisotropy, the reflection coefficients of P-wave are concisely denoted as the analytic function of fracture parameters. For the purpose of predicting the coal-bed fracture distribution through analyzing variation of the reflection amplitudes with offset and azimuth, 3-D seismic data with full-azimuth were acquired in a coal field in Huainan, Anhui Province. The careful analysis and process of seismic data showed that the reflection amplitude of the primary coalbed varied with azimuth in much consistent with the theoretical model. The conclusion was drawn that the coal-bed fracture in this coal field could be predicted through the method of the P-wave azimuthal AVO.