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Inductive charging, a form of wireless charging, uses an electromagnetic field to transfer energy between two objects. This emerging technology offers an alternative solution to users having to physically plug in their electric vehicle (EV) to charge. Whilst manufacturers claim inductive charging technology is market ready, the efficiency of transf...
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... aids are deigned to assist specifically with the longitudinal aspect of parking. Results showed that parking sensors significantly (F (2,89) = 8.83, p < 0.001) increased the accuracy, as defined by the distance of the centre point of the vehicle from the centre point of the bay, of parking alignment in this study (Fig. 7). Table 4 shows results from the dynamic study. Parking was generally more accurate in the lateral direction when aligning the front of the vehicle over the charging pad in comparison to the centre of the vehicle (mean x-displacement of 0.5 verses 7.3 cm respectively). This difference was statistically significant (F (1,19) = 9.02, p < ...Context 2
... difference which was significant (p < 0.05). Small vehicles in particular tended to park towards the rear of the bay com- pared to large vehicles. The spread of data in the lateral direction is similar for all sizes of vehicles. There is a systemic dif- ference in the vehicles which may account for the accuracy differences observed. As we see in Fig. 7, the presence of parking sensors increased the accuracy of parking alignment. Whilst parking sensors inform the driver of their distance to a rear bar- rier, and not the centre of the bay, the real-world application of this suggested that those without sensors were more likely to overhang the front of the bay or misjudge their ...Similar publications
To reduce the dependence on oil and environmental pollution, the development of electric vehicles has been accelerated in many countries. The implementation of EVs, especially battery electric vehicles, is considered a solution to the energy crisis and environmental issues. This paper provides a comprehensive review of the technical development of...
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... It tends to last only 3 s, resulting in high risk because of it increasing the workload and pressure on drivers [1]. Data indicate that traffic accidents caused by lane-changing behavior take up about 5% of the traffic accident rate and 7% of accidental deaths [2][3][4]. However, if drivers are warned 0.5 s before the accident, the accident rate could be reduced by 60%. ...
Lane changing is a crucial scenario in traffic environments, and accurately recognizing and predicting lane-changing behavior is essential for ensuring the safety of both autonomous vehicles and drivers. Through considering the multi-vehicle information interaction characteristics in lane-changing behavior for vehicles and the impact of driver experience needs on lane-changing decisions, this paper proposes a lane-changing model for vehicles to achieve safe and comfortable driving. Firstly, a lane-changing intention recognition model incorporating interaction effects was established to obtain the initial lane-changing intention probability of the vehicles. Secondly, by accounting for individual driving styles, a lane-changing behavior decision model was constructed based on a Gaussian mixture hidden Markov model (GMM-HMM) along with a parameter estimation method. The initial lane-changing intention probability serves as the input for the decision model, and the final lane-changing decision is made by comparing the probabilities of lane-changing and non-lane-changing scenarios. Finally, the model was validated using real-world data from the Next Generation Simulation (NGSIM) dataset, with empirical results demonstrating its high accuracy in recognizing and predicting lane-changing behavior. This study provides a robust framework for enhancing lane-changing decision making in complex traffic environments.
... Considering the fact that the charging components are not within the driver's field of vision, reaching a minimal deviation of the coils is a challenging task. Accordingly, Birrel et al. [7] found in studies that only 5% of the vehicles achieved an accurate position that allowed efficient wireless charging. There are several techniques to reduce misalignment of charging components, such as mechanical [26], RFID [38], or wireless sensor-based methods [47]. ...
Wireless charging of electric vehicles can be achieved by installing a transmitter coil into the ground and a receiver coil at the underbody of a vehicle. In order to charge efficiently, accurate alignment of the charging components must be accomplished, which can be achieved with a camera-based positioning system. Due to an air gap between both charging components, foreign objects can interfere with the charging process and pose potential hazards to the environment. Various foreign object detection systems have been developed with the motivation to increase the safety of wireless charging. In this paper, we propose a foreign object detection technique that utilizes the integrated camera of an embedded positioning system. Due to operation in an outdoor environment, we cannot determine the types of objects that may occur in advance. Accordingly, our approach achieves object-type independence by learning the features of the charging surface, to then classify anomalous regions as foreign objects. To examine the capability of detecting foreign objects, we evaluate our approach by conducting experiments with images depicting known and unknown object types. For the experiments, we use an image dataset recorded by a positioning camera of an operating wireless charging station in an outdoor environment, which we published alongside our research. As a benchmark system, we employ YOLOv8 (Jocher et al. in Ultralytics YOLO, 2023), a state-of-the-art neural network that has been used in various contexts for foreign object detection. While we acknowledge the performance of YOLOv8 for known object types, our approach achieves up to 18% higher precision and 46% higher detection success for unknown objects.
... With EVs gaining popularity, aided by government incentives, there's a growing demand for user-friendly charging solutions [1]. Wireless Power Transfer (WPT) emerges as a compelling alternative to conventional cable charging, where drivers park their vehicles over a ground-embedded TX coil, initiating charging through a chassis-mounted RX coil[2]. ...
Wireless power transfer (WPT) has the potential to revolutionise global transportation and to catalyse the rise of the market for electric vehicles (EVs) by providing a compelling alternative to the conventional ways of charging that involve the use of cables. Nevertheless, coil misalignment, which is caused by the behaviours of drivers who park their vehicles, is a substantial challenge since it has a detrimental impact on power transfer efficiency (PTE). This work presents a unique coil design that is coupled with adaptive hardware in order to improve power transfer efficiency (PTE) in magnetic resonant coupling waste power transfer (WPT) systems and reduce the impacts of coil misalignment, which is a significant impediment that is preventing the broad implementation of WPT for electric vehicles (EVs). Simulation with ADS was used to validate the effectiveness of the new design, which exhibited a high degree of congruence with the theoretical analysis. In addition, receiver and transmitter circuitry that was specifically constructed for the purpose of simulating real-world vehicle and parking bay settings was utilised. This allowed for the collecting of PTE data in a controlled environment that was on a smaller scale. Experiments showed that there was a significant thirty percent improvement in the power transfer efficiency (PTE) at the centre of the coil array, and an astonishing ninety percent improvement was recorded when the array was misaligned by three quarters of its radius. When compared to single coil designs that serve as benchmarks, the suggested new coil array displays improved property transfer efficiency (PTE).
... According to the observatory study in [5], lateral misalignments of more than ±200 mm are expected while a vehicle is on-the-move. [6] also reveals that 90 − 120 mm of misalignments are expected for parked vehicles. This misalignment reduces the coupling between the transmitter and receiver pads, and decreases the amount of power received by the EV. ...
... The range is shortened at high speeds and extended at low speeds. Birrell et al. [36], Yang et al. [37], and Marmaras et al. [38]examined the relationship between drivers' driving behaviors and range. Companies must consider finding the size and characteristics of the fleet, such criteria as the customers' demands, the characteristics and amount of the loads to be transported, the legal obligations in the region to distribute, and the physical characteristics of the demand points. ...
The Electric Vehicle Routing Problem (EVRP) is an extension of the Vehicle Routing Problem (VRP), wherein electric vehicles (EVs) are utilized instead of internal combustion engine vehicles (ICEVs). Electric vehicles have a limited driving range due to their battery capacity and require recharging to complete their routes. Charging can take place at any battery level and can be done up to the battery capacity. Furthermore, the charging speed may vary depending on the technical infrastructure of the charging station (CS). In certain real-life applications, battery swap stations (BSSs) are used in conjunction with charging stations. This study specifically focuses on articles that discuss the use of electric vehicles in logistics activities, where they are charged during the route or through battery swapping. Firstly, the electric vehicle routing problem is introduced, explaining the evolution from the vehicle routing problem to the electric vehicle routing problem. Subsequently, a mathematical model for the electric vehicle routing problem is presented. The literature on the electric vehicle routing problem is then summarized using data and visuals, and classified based on different characteristics such as assumptions, constraints, problem types, and solution approaches. The emphasis is placed on the notable aspects and solution approaches within each category. Finally, future research opportunities are summarized.
... The position detection of the receiver is realized by monitoring the primary current [20]. The receiver coil of the WPT system can also be positioned by GPS, infrared detection, ultrasonic positioning, machine vision, radio frequency identification, image processing, and other technologies [21][22][23][24][25]. However, by adding additional detection coils and sensors or using other technologies, the cost and complexity of the WPT system will increase. ...
This paper proposes a position recognition method for the receiver coil by the secondary coil current. Based on the practical value of the current of the receiver coil, the position recognition of the receiver coil in the WPT system is realized. This paper proposes a 3×3 array multi-transmitter coil grouping and control logic. The mathematical model of mutual inductance between the receiver and transmitter coil at different positions on the X-Y plane is established. A method for identifying the position of the receiver coil according to the current of the receiver coil is proposed. Compared with the traditional position recognition method of the receiver coil, this method does not need to add a detection coil and position sensor and can realize the position recognition of the receiver coil on the 2D plane.
... Studies have further shown that automated parking systems significantly reduce workload and stress for human drivers (Reimer et al., 2010;Reimer et al., 2016). Another benefit of these systems may be that they help to eliminate the inherent human variability in parking, which can prevent electric vehicles from efficiently charging themselves (Birrell, Wilson, Yang, Dhadyalla, & Jennings, 2015). Lastly, effective automation parking systems may reduce thousands of injuries that occur in parking facilities yearly (N. ...
In two studies, we evaluated the trust and usefulness of automated compared to manual parking using an experimental paradigm and also by surveying owners of vehicles with automated parking features. In Study 1, we compared participants' ability to manually park a Tesla Model X to their use of the Autopark feature to complete perpendicular and parallel parking maneuvers. We investigated differences in parking success and duration, intervention behavior, self-reported levels of trust in and workload associated with the automation, as well as eye and head movements related to monitoring the automation. We found higher levels of trust in the automated parallel parking maneuvers compared to perpendicular parking. The Tesla's automated perpendicular parking was found to be less efficient than manually executing this maneuver. Study 2 investigated the frequency with which owners of vehicles with automated parking features used those features and probed why they chose not to use them. Vehicle owners reported low use of any automated parking feature. Owners further reported using their vehicle's autonomous parking features in ways consistent with the empirical findings from Study 1: higher usage rates of autonomous parallel parking. The results from both studies revealed that 1) automated parking is error-prone, 2) drivers nonetheless have calibrated trust in the automated parking system, and 3) the benefits of automated parallel parking surpass those of automated perpendicular parking with the current state of the technology.
... Although road fatalities have dropped by 1.7% from 2019 to 2021 across China, over 60,000 people lost their lives, and over two million were seriously injured yearly [1]. Most traffic accidents are caused by issues involving human drivers, such as cognitive overload, misjudgment and misoperation [2], [3]. Especially at intersections, traffic accidents are more prone to occur due to complex traffic [4]. ...
Accurate and reliable prediction of driving intentions and future trajectories contributes to cooperation between human drivers and ADAS in complex traffic environments. This paper proposes a visual AOI (Area of Interest) based multimodal trajectory prediction model for probabilistic risk assessment at intersections. In this study, we find that the visual AOI implies the driving intention and is about 0.6-2.1 s ahead of the operation. Therefore, we designed a trajectory prediction model that integrates the driving intention (DI) and the multimodal trajectory (MT) predictions. The DI model was pre-trained independently to extract the driving intention using features including the visual AOI, historical vehicle states, and environmental context. The intention prediction experiments verify that the visual AOI-based DI model predicts steering intention 0.925 s ahead of the actual steering operation. The trained DI model is then integrated into the trajectory prediction model to filter multimodal trajectories. The trajectory prediction experiments show that the proposed model outperforms the state-of-the-art models. Risk assessment for traffics at intersections verifies that the proposed method achieves high accuracy and a low false alarm rate, and identifies the potential risk about 3 s before a conflict occurs.
... Inductive charging efficiency is highly related to the charging coils' position relative to one another, with a tolerance of approximately ±10cm from the center points. In contrast, studies have shown that human parking accuracy is between 20 and 120cm longitudinally and 20 to 60cm laterally, with only 5% of vehicles parked within the tolerances for optimal efficiency [4]. ...
... However, we manually evaluated the final alignment of the vehicle against the chargepad on a small number of scenes (three indoor and three outdoor scenes), as presented in Table VI. It can be seen that at least five of the six scenes tested, we are within the ±10cm alignment accuracy required for optimal charging [4], with the final scene being only just outside. The alignment accuracy is slightly higher for indoor scenes, as the lighting tends to be more controlled. ...
Electric Vehicles are increasingly common, with inductive chargepads being considered a convenient and efficient means of charging electric vehicles. However, drivers are typically poor at aligning the vehicle to the necessary accuracy for efficient inductive charging, making the automated alignment of the two charging plates desirable. In parallel to the electrification of the vehicular fleet, automated parking systems that make use of surround-view camera systems are becoming increasingly popular. In this work, we propose a system based on the surround-view camera architecture to detect, localize, and automatically align the vehicle with the inductive chargepad. The visual design of the chargepads is not standardized and not necessarily known beforehand. Therefore, a system that relies on offline training will fail in some situations. Thus, we propose a self-supervised online learning method that leverages the driver’s actions when manually aligning the vehicle with the chargepad and combine it with weak supervision from semantic segmentation and depth to learn a classifier to auto-annotate the chargepad in the video for further training. In this way, when faced with a previously unseen chargepad, the driver needs only manually align the vehicle a single time. As the chargepad is flat on the ground, it is not easy to detect it from a distance. Thus, we propose using a Visual SLAM pipeline to learn landmarks relative to the chargepad to enable alignment from a greater range. We demonstrate the working system on an automated vehicle as illustrated in the video
https://youtu.be/_cLCmkW4UYo
. To encourage further research, we will share a chargepad dataset used in this work (an initial version of the dataset is shared
https://drive.google.com/drive/folders/1KeLFIqOnhU2CGsD0vbiN9UqKmBSyHERd
here).
... Studies published in the field of road safety have shown that most traffic accidents are caused by inappropriate behavior, such as driver operation errors [1]. Correctly understanding the driver's intention is considered to be an effective way to reduce traffic injuries and improve traffic safety [2]. ...
... In the last stage, the outputs of the two pathways are input into a global average pooling layer; that is, an average value is calculated for the features of each channel. At this time, the time dimension and height and width are set to [αN, 1,1]. Therefore, the shapes of the output features of SP and FP are {αN, 1 2 , 2048} and {αN, 1 2 , 256}, respectively. ...
... In the last stage, the outputs of the two pathways are input into a global average pooling layer; that is, an average value is calculated for the features of each channel. At this time, the time dimension and height and width are set to [αN, 1,1]. Therefore, the shapes of the output features of SP and FP are {αN, 1 2 , 2048} and {αN, 1 2 , 256}, respectively. ...
In order that fully self-driving vehicles can be realized, it is believed that systems where the driver shares control and authority with the intelligent vehicle offer the most effective solution. An understanding of driving intention is the key to building a collaborative autonomous driving system. In this study, the proposed method incorporates the spatiotemporal features of driver behavior and forward-facing traffic scenes through a feature extraction module; the joint representation was input into an inference module for obtaining driver intentions. The feature extraction module was a two-stream structure that was designed based on a deep three-dimensional convolutional neural network. To accommodate the differences in video data inside and outside the cab, the two-stream network consists of a slow pathway that processes the driver behavior data with low frame rates, along with a fast pathway that processes traffic scene data with high frame rates. Then, a gated recurrent unit, based on a recurrent neural network, and a fully connected layer constitute an intent inference module to estimate the driver’s lane-change and turning intentions. A public dataset, Brain4Cars, was used to validate the proposed method. The results showed that compared with modeling using the data related to driver behaviors, the ability of intention inference is significantly improved after integrating traffic scene information. The overall accuracy of the intention inference of five intents was 84.92% at a time of 1 s prior to the maneuver, indicating that making full use of traffic scene information was an effective way to improve inference performance.
