Real Car Research Articles

Control of Fuel Lithium Battery Hybrid Vehicle Power Supply System and Vehicle Energy Management Strategy

<p>The main research object of this article is the increasing number of new energy vehicles, focusing on the energy management issues of new energy vehicles. Firstly, a real car model was established: an engine mathematical model, an electric motor mathematical model, and a power battery mathematical model. Then, in order to achieve optimal management of vehicle energy management, an algorithm model for vehicle energy dynamic planning was established with the goal of minimizing energy consumption. Finally, simulation experiments were conducted to verify the performance improvement of the proposed algorithm in vehicle energy management.</p> <p> </p>

Parking behaviour under the influence of alcohol

Real-life driving studies evaluating the impact of alcohol influence on the ability to park a car are rare but necessary to assess a possible impairment to drive a car in the event of prosecution. In this study, 29 test persons (13 m, 16 f) completed three test drives with real cars, each made up of three different parking situations. While four test persons remained sober, the majority drank a previously calculated amount of alcohol before the second drive; the aim was to reach a blood alcohol concentration (BAC) of 1.1 g/kg. The third drive took place about 2 h later without any further ingestion of alcohol. The impact of BAC on the number of accidents, time needed to finish the drive, the amount of correction moves and quality of the final parking position (in the centre of the parking space) were analysed. Furthermore, pressure measuring films were applied to the test cars, measuring the average pressure and load in the areas of the accident impact. A significant increase of accidents could be noted with rising BAC. While a single accident happened to both sober and drivers under the influence of alcohol, more than one accident was only seen in drivers after the ingestion of alcohol (> 0.63 g/kg). The BAC had no impact on the other considered aspects. Concludingly, more than one impact site or accident while parking a car can serve as an indication for alcohol impairment of the driver at the time of the accident.

Enhancing Driving Ability in Older Adults through Health Exercises and Physical Activity: A Randomized Controlled Trial.

The global rise in the aging driving population has heightened concerns about traffic incidents involving this demographic. Beyond transportation, automobiles represent a vital lifeline for older adults, fostering social activities and influencing their health-related quality of life. This study explores improving and sustaining driving ability among older adults with anticipated declines through health-conscious exercises. Sixty-eight participants were randomly allocated into two groups. The exercise-oriented group (E-group) engaged in twelve 90 min health and exercise sessions over twelve weeks, while the control group (C-group) maintained their regular daily routines and did not receive any specific interventions during this period. The focal point of assessment was driving ability, as evaluated by a person using a real car on public roads without using a simulator. Driving ability and physical fitness were assessed before the intervention in both groups. Post-intervention measurements occurred twelve weeks after the initial gauging, encompassing both cohorts. Comparative analysis of pre- and post-intervention changes was executed between the two groups. The E-group demonstrated improved overall driving ability compared to the C-group. The results suggest that healthy exercise and physical activity may maintain and enhance driving ability for older adults.

Battery-Free Wireless Torque Sensor Powered by RF Energy

Conventional dynamic torque measurement usually requires wires and slip rings for supply and data transmission, which shows many drawbacks in terms of device installation, maintenance, durability, and reliability. We here present a wireless torque measurement sensor without a battery, which can achieve a long-term and real-time torque monitoring on either static or dynamic rotation systems. The proposed battery-free wireless torque sensor (BWTS) is non-destructive and low-cost. The BWTS is equipped with a Koch-Meander hybrid dipole antenna to receive the radiofrequency (RF) energy emitted from a customized reader. The BWTS can be activated at an input power of -15 dBm (32 lW), and the maximum operational distance in the open air is 3 meters. We also study the sampling rate of the BWTS on a dynamic shaft and demonstrate it on the road, which to our best knowledge, is the first successful demonstration of fully self-powered and wireless torque monitoring in a real running car. This work indicates the feasibility of wirelessly powering torque sensors using far-field RF energy, introducing a new power solution to wireless and batteryless automotive sensors.

Research on Differential Steering Dynamics Control of Four-Wheel Independent Drive Electric Tractor

Traditional tractors can only achieve steering through mechanical structures such as steering knuckles and steering trapezoids. Among them, the mechanical structure is more complex, and various parts are easily damaged, making the tractor malfunction. The four-wheel independent drive differential steering mode differs from the traditional Ackermann steering mode, which realizes steering by controlling the inner and outer wheel torque, which can accurately steer the working state of high-end agricultural machinery equipment and improve the operating efficiency of agricultural machinery equipment. Aiming at the dynamic control problem in the steering of electric tractor four-wheel independent drive, a layered control strategy based on the sliding mode control of yaw torque at the upper level and the optimal torque distribution level based on the mean load rate of vehicle tires at the lower was proposed. By analyzing the differential steering mechanism of a four-wheel independent drive, a dynamic model of differential steering of the electric tractor is established, and a dynamic controller of a four-wheel independent drive is designed according to the layered control strategy. The upper controller tracks and controls the expected yaw speed on the basis of the sliding mode control to track the driver’s intention, and the lower controller realizes the optimal torque distribution based on the principle of the optimal average load rate of the vehicle tire to ensure the steering stability of the electric tractor. The effect of the controller was simulated and analyzed under typical conditions of double line shift, serpentine, and step. The results showed that the sliding mode controller is better than the PID controller in driver intention tracking. Compared with the average allocation strategy, the average maximum load rate of the vehicle tire under the three working conditions is reduced by 16.9%, 13.8%, and 17.3%, respectively, which proves the effectiveness of the layered control strategy. In the real car test, the sliding mode controller is better than the PID controller in the driver intention tracking. This study has important guiding significance for improving the maneuverability and stability of electric tractors.

End-to-End Approach for Autonomous Driving: A Supervised Learning Method Using Computer Vision Algorithms for Dataset Creation

This paper presents a solution for an autonomously driven vehicle (a robotic car) based on artificial intelligence using a supervised learning method. A scaled-down robotic car containing only one camera as a sensor was developed to participate in the RoboCup Portuguese Open Autonomous Driving League competition. This study is based solely on the development of this robotic car, and the results presented are only from this competition. Teams usually solve the competition problem by relying on computer vision algorithms, and no research could be found on neural network model-based assistance for vehicle control. This technique is commonly used in general autonomous driving, and the amount of research is increasing. To train a neural network, a large number of labelled images is necessary; however, these are difficult to obtain. In order to address this problem, a graphical simulator was used with an environment containing the track and the robot/car to extract images for the dataset. A classical computer vision algorithm developed by the authors processes the image data to extract relevant information about the environment and uses it to determine the optimal direction for the vehicle to follow on the track, which is then associated with the respective image-grab. Several trainings were carried out with the created dataset to reach the final neural network model; tests were performed within a simulator, and the effectiveness of the proposed approach was additionally demonstrated through experimental results in two real robotics cars, which performed better than expected. This system proved to be very successful in steering the robotic car on a road-like track, and the agent’s performance increased with the use of supervised learning methods. With computer vision algorithms, the system performed an average of 23 complete laps around the track before going off-track, whereas with assistance from the neural network model the system never went off the track.

VitroBench: Manipulating in-vehicle networks and COTS ECUs on your bench: A comprehensive test platform for automotive cybersecurity research

With the increasing connectivity employed in automotive systems, remote cyber attacks have now become a possibility and concrete threat. Prior works on automotive cyber security solutions have primarily focused evaluation either on real cars or via emulations of electronic control units (ECUs). Evaluation on real cars offers limited flexibility in manoeuvring the packets communicated through in-vehicle network (IVN). Meanwhile, emulations of ECUs rely on assumptions that may not correspond to the exact features in an IVN.In this paper, we present VitroBench, a comprehensive test platform involving commercial off-the-shelf (COTS) ECUs that allows arbitrary packet control over IVN. In contrast to existing automotive testbed, an appealing feature of VitroBench is that it allows replication of driving use cases and scenarios directly on the testbed involving COTS ECUs. This, in turn, allows us to design and evaluate concrete attacks that are directly related to a driving scenario. We present the design of VitroBench that allows us to sniff, inject packets to and isolate targeted ECU via bridging. The isolation of ECUs also offers fuzzing the respective ECUs. We evaluate the capability of VitroBench via launching concrete attacks and demonstrating the impact of such attacks. We discuss the careful design choices involved in VitroBench that inspire automotive cybersecurity research in future.

Fidelity Assessment of Motion Platform Cueing: Comparison of Driving Behavior under Various Motion Levels.

The present paper focuses on vehicle simulator fidelity, particularly the effect of motion cues intensity on driver performance. The 6-DOF motion platform was used in the experiment; however, we mainly focused on one characteristic of driving behavior. The braking performance of 24 participants in a car simulator was recorded and analyzed. The experiment scenario was composed of acceleration to 120 km/h followed by smooth deceleration to a stop line with prior warning signs at distances of 240, 160, and 80 m to the finish line. To assess the effect of the motion cues, each driver performed the run three times with different motion platform settings-no motion, moderate motion, and maximal possible response and range. The results from the driving simulator were compared with data acquired in an equivalent driving scenario performed in real conditions on a polygon track and taken as reference data. The driving simulator and real car accelerations were recorded using the Xsens MTi-G sensor. The outcomes confirmed the hypothesis that driving with a higher level of motion cues in the driving simulator brought more natural braking behavior of the experimental drivers, better correlated with the real car driving test data, although exceptions were found.

Highly reproducible eyeblink timing during formula car driving

How do humans blink while driving a vehicle? Although gaze control patterns have been previously reported in relation to successful steering, eyeblinks that disrupt vision are believed to be randomly distributed during driving or are ignored. Herein, we demonstrate that eyeblink timing shows reproducible patterns during real formula car racing driving and is related to car control. We studied three top-level racing drivers. Their eyeblinks and driving behavior were acquired during practice sessions. The results revealed that the drivers blinked at surprisingly similar positions on the courses. We identified three factors underlying the eyeblink patterns: the driver's individual blink count, lap pace associated with how strictly they followed their pattern on each lap, and car acceleration associated with when/where to blink at a moment. These findings suggest that the eyeblink pattern reflected cognitive states during in-the-wild driving and experts appear to change such cognitive states continuously and dynamically.

Numerical analysis of unsteady flow features around a realistic Estate vehicle with hybrid RANS/LES methods

A comparative study is conducted to analyze the flow around a realistic Peugeot Estate car (308 SW) using RKE and SST RANS models, as well as DDES hybrid RANS/LES approach. The authors used previous work on 25° Ahmed bodies to establish the best grid and DDES parameters for external aerodynamics prediction. The aim of this work is to extend and transpose the numerical methodology to a realistic car. Numerical and physical characteristics related to the simulation of this complex flow are therefore carefully investigated. The numerical results are compared to in-house experiments carried out in S2A Stellantis Wind Tunnel. Two grids with 115 and 185 million elements, based on 20 prism layers cells near the wall, are used to compare the three turbulence models. Even with a smaller grid size in the wake and underbody, the predictions of drag and lift coefficients are almost identical. Moreover, refining the near-wall region from 20 to 30 prism layers has no effect on drag coefficient and flow prediction. Consequently, the grid with 20 prism layers and 115M cells is used as reference grid in this work. Then, the flow features between RKE, SST and SST DDES models are compared to two-dimensional (2D) and three-dimensional (3D) experimental results. RANS gives better agreement on aerodynamics forces. However, DDES reproduces accurately the specific three-dimensional topology of the flow around the car, in the wake and the underbody when RANS results are globally wrong.

P2P Cloud Manufacturing Based on a Customized Business Model: An Exploratory Study

To overcome the problems of long production cycle and high cost in the product manufacturing process, a P2P (platform to platform) cloud manufacturing method based on a personalized custom business model has been proposed in this paper by integrating different technologies such as deep learning and additive manufacturing (AM). This paper focuses on the manufacturing process from a photo containing an entity to the production of that entity. Essentially, this is an object-to-object fabrication. Moreover, based on the YOLOv4 algorithm and DVR technology, an object detection extractor and a 3D data generator are constructed, and a case study is carried out for a 3D printing service scenario. The case study selects online sofa photos and real car photos. The recognition rates of sofa and car were 59% and 100%, respectively. Retrograde conversion from 2D data to 3D data takes approximately 60 s. We also carry out personalized transformation design on the generated sofa digital 3D model. The results show that the proposed method has been validated, and three unindividualized models and one individualized design model have been manufactured, and the original shape is basically maintained.

Design of air conditioner (AC) system simulator on cars to improve student competence

The purpose of this study is to design a simulator for a car air conditioning system as a learning medium, so that the learning objectives can be achieved properly and in accordance with the expected competencies. This design uses an electric motor drive to drive the AC system compressor so that it is easier to operate and does not produce air pollution and is made with a design that can be moved easily by having sturdy wheels. The electric motor is also equipped with an inverter that can adjust the rotation of the motor as in the conditions of a real car engine. This research model uses Research and Development which includes the stages: problem analysis, seeking information, designing a model, validating the design, evaluating the model, testing the model, revising the model, wider testing, finalizing the design. The results obtained are the creation of an AC simulator that has been validated by experts in terms of media and material in the very feasible category, then from the user side with 25 respondents giving a value in the very feasible category for use in learning. The increase in competency occurred by 23.90% with a comparison of the pretest value with the posttest value.

LiDAR-as-Camera for End-to-End Driving

The core task of any autonomous driving system is to transform sensory inputs into driving commands. In end-to-end driving, this is achieved via a neural network, with one or multiple cameras as the most commonly used input and low-level driving commands, e.g., steering angle, as output. However, simulation studies have shown that depth-sensing can make the end-to-end driving task easier. On a real car, combining depth and visual information can be challenging due to the difficulty of obtaining good spatial and temporal alignment of the sensors. To alleviate alignment problems, Ouster LiDARs can output surround-view LiDAR images with depth, intensity, and ambient radiation channels. These measurements originate from the same sensor, rendering them perfectly aligned in time and space. The main goal of our study is to investigate how useful such images are as inputs to a self-driving neural network. We demonstrate that such LiDAR images are sufficient for the real-car road-following task. Models using these images as input perform at least as well as camera-based models in the tested conditions. Moreover, LiDAR images are less sensitive to weather conditions and lead to better generalization. In a secondary research direction, we reveal that the temporal smoothness of off-policy prediction sequences correlates with the actual on-policy driving ability equally well as the commonly used mean absolute error.

An innovative method for scene recognition and target tracking for adaptive cruise control in complex traffic environment

The adaptive cruise control (ACC) system as a typical advanced driver assistant system (ADAS) has been commercially application in automotive industry for decades. An innovative method is proposed in this paper for scene recognition and target tracking for ACC application in some complex traffic environment. Firstly, a multi-sensor fusion method is established to estimate the curvature integrated by the quadratic programing (QP)-based lane boundaries detection, vehicle dynamics of lateral motion, and an improved Kalman filter (IKF) to introduce more measurement information into the feedback correction process. Then, the closet in-path vehicle (CIPV) can be selected according to the statistical distance between the tracked targets and the predicted driving path of ego vehicle. To distinguish the lane changing and curve driving behaviors, the trajectory models of obstacles are established as an ellipsoid domain equation and transformed into a regression model, which is recast as a standardized QP problem. Hence, the behaviors and scenes can be recognized effectively. To restrain the disturbance and improve the accuracy and robustness of target tracking, an [Formula: see text]-based switched tracking method is proposed by combining of the low pass filter (LPF) and [Formula: see text] theory. Finally, an accurate and robust tracker is provided for the CIPV by incorporating with four steps: IKF-based curvature fusion estimation, CIPV selection, QP-based scene recognition, and the [Formula: see text]-based observer. Moreover, two real car experiments are adopted and the results verify the effectiveness and real-time performance of the proposed method.

Comparison of flow characteristics behind squareback bluff-bodies with and without wheels

The wake dynamics of two referenced variations of the squareback Windsor model with and without wheels is numerically studied by performing improved delayed detached eddy simulation. Numerical assessments are validated against publicly available experimental data. The focus of this study is on the wake states influenced by the wheels and the thick oncoming floor boundary layer. Results show that the addition of the wheels significantly changes the aerodynamic forces, the underbody flow, and the wake topology. The wake bi-stability is also enhanced with wheels in place due to the increased curvature of lateral shear layers in the near wake. However, the bi-stable behavior is largely suppressed when immersed in a thick boundary layer. These alterations depend on the degree of interaction between the wake recirculation and the bottom flow, and such degree is strongly affected by the underbody flow momentum. The evolution of low-order flow organizations and complementary spectral analysis highlight the differences in the coherent dynamics of the wake. The finding of this present work suggests that the wake bi-stability behind the squareback body can exist not only for a simplified geometry but also for a more realistic car with wheels in real-world upstream conditions.

Combined single-pedal and low adhesion control systems for enhanced energy regeneration in electric vehicles: Modeling, simulation, and on-field test

In this paper, a single-pedal control algorithm was proposed, modeled, and theoretically analyzed to improve the driving range of electric vehicles and increase the intensity and frequency of energy regeneration. Specific analysis and strategy improvement were also conducted for the control problem under low adhesion conditions. The driving control function, economy, and control logic of the proposed single-pedal system were investigated using both simulations and real vehicle verifications under the New European Driving Cycle (NEDC). Based on simulations and real car testing, optimization of the single-pedal system under the traditional low adhesion control strategy was conducted, and then comprehensive comparisons were investigated. Results demonstrated that the proposed single-pedal control and its low adhesion condition control strategy are in line with the design requirements and effectively improved the vehicle economy, achieving a 24.9% energy regeneration rate. The optimized single-pedal low adhesion control strategy, according to the results, can effectively satisfy driving smoothness, economy, and stability under low adhesion conditions.

Attitude Control of Vehicle Based on Series Active Suspensions

When vehicles with traditional passive suspension systems are driving in complex terrain, large swing and vibrations of the car body make passengers and goods uncomfortable and unstable, even at very low-speed conditions. Considering the actual need for intelligent resource exploration in the sustainable economy, visual-based perception and localization systems of unmanned vehicles still cannot handle the sensor noise coursed by large body motions. In order to improve the stability and safety of vehicles in complex terrain, an attitude control system is proposed for mainly eliminating the external body motions of the vehicle by using series active suspensions. A model predictive control method considered the differences between the simulated and real vehicle, and the performance restrictions of actuators are used to design the attitude controller for reducing the heaving, pitching, and rolling motions of the vehicle. After simulations and real car tests, the results show that the proposed attitude controller can significantly improve the attitude stability of vehicles in harsh terrain.

Optimum Real Time Estimation and Interfacing of Braking Points for Racing Car

To provide additional on-track support for racing drivers, a novel real-time braking point estimation system was developed and tested to improve overall lap time performance. The Formula-Student competition was chosen as testing environment because of direct access to a real car and, hence, to all the data needed for this study, which can be fully disclosed at the same time. To estimate the optimum braking point, a simulation of the track section ahead is performed using a multidimensional approach to model not only longitudinal and lateral acceleration, but also combined acceleration scenarios. A lap identification system as well as live track analysis tools were developed in order to obtain track data that cannot be measured directly. To evaluate the overall grip conditions, a tire model, using both a wheel load and a two-track model, is implemented, which includes a dynamic calibration feature designed to adapt the system to the changing conditions of the circuit. It can be seen that the simulation provides a detailed profile of the maximum permitted velocities for every point in front of the real vehicle, and that this can be used to create a reliable prediction of the braking points along the track. Furthermore, a concept based on physiological fundamentals was developed and investigated in terms of human reaction times in order to meet the requirements of an intuitive human-machine interface. Our results demonstrate that it can be successfully used to automatically compensate for driver delays, improve the trust in the system and ensure safe operation. We conclude that our new system can reduce lap times, depending on the length and number of curves, by a significant amount of time (tens of a second).

Suspension Control Strategies Using Switched Soft Actor-Critic Models for Real Roads

In this article, we propose learning and control strategies for a semiactive suspension system in a full car using soft actor-critic (SAC) models on real roads, where many road profiles with various power of disturbance exist (e.g., speed bumps and general roads). Therefore, a technique that enables deep reinforcement learning to cover different domains with largely different reward functions is proposed. This concept was first realized in a simulation environment. Our proposed switching learning system continuously identifies two different road disturbance profiles in real time such that the appropriately designed SAC model can be learned and applied accordingly. The results of the proposed switching SAC algorithm were compared against those of advanced and conventional benchmark suspension systems. Based on the results, the proposed algorithm showed smaller root-mean-square values of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$z$</tex-math></inline-formula> -directional acceleration and pitch at the center of the body mass. Finally, we also presented our successfully implemented SAC training system in a real car on real roads. The trained SAC model outperforms conventional controllers reducing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$z$</tex-math></inline-formula> -directional acceleration and pitch, similar to the simulation results, which is highly related to the riding comfort and vehicle maneuverability.

Adaptive Motion Cueing Algorithm Using Optimized Fuzzy Control System for Motion Simulators

The Motion Cueing Algorithm (MCA) is the main unit of motion simulators responsible for transforming the vehicle motions to generate driving motion sensation for the simulator users within the motion simulator's physical workspace through washout filters. In this study, we design and provide a new framework by developing a set of novel washout filters using optimized fuzzy control systems to solve the drawbacks associated with the existing optimal MCAs of the motion simulators. These drawbacks include constant washout filter parameters, inefficient simulator movement in the workspace, lack of considering the simulator's physical constraints and driver sensation-related factors, and sub-optimal settings which causes false motion cues and subsequently simulator sickness. As a pioneering framework, the fuzzy logic controllers generate restitution signals for the optimal washout filters based on the motion sensation error between the real vehicle and simulator divers as well as the platform's position in the workspace aiming to correct the filtered signals, reduce error, generate realistic motions, and increase motion fidelity online. The GA is applied to optimize the fuzzy logic membership functions and control rules. The optimization procedure takes into account a number of important factors that are normally ignored in the current MCAs including the sensed motion error between the drivers of simulator and real car; the platform physical constraints; the motion threshold limiter effects; and signal shape following. The new adaptive MCA is designed, implemented and tested using the MATLAB/Simulink. The simulation results confirm the effectiveness of the developed MCA in maximizing motion fidelity and the reference sensation shape following, and enhancing the workspace usage of the simulator.