Vehicle Front Research Articles

Determination of fire protection distances during a tesla model s fire in a closed parking lot

This study modelled an electric vehicle fire on the example of a Tesla Model S (USA) in a closed car park. Such fires pose an increased danger due to their rapid spread, the presence of a large number of vehicles, the release of toxic combustion products and heavy smoke. In fact, the rapid spread of a fire in a closed car park is caused by unreasonably small distances between vehicles. Thus, the purpose of the study was to determine the minimum fire protection distances due to an electric vehicle fire in a closed car park using the example of Tesla Model 3. For this purpose, the objects and their physical characteristics were described, input and environmental parameters were set, and a mathematical model of the dynamics of fire development was formed. This made it possible to establish the minimum fire protection distance during the free development time of 610 s for an electric vehicle fire in a closed car park, which is 10 m along the flank and 6 m along the front. The difference in fire protection distances on the flank and front is explained by the design features of the electric vehicle. That is, the flank area of the flame will be larger than the front of the burning electric vehicle, and therefore the heat radiation power will also be greater. The actual heat radiation power is the key factor affecting fire protection distances. The results of the study can be used in the design of various types of car parks and the safe placement of vehicles in them. Fire protection distances between cars in enclosed car parks can be taken into account by insurance companies when assessing the risk of damage to cars due to fires. And also by fire and rescue units involved in extinguishing such fires, to ensure the safety of rescuers

Effects of an integrated safety system for swivel seat arrangements in frontal crash.

Objective: Autonomous vehicles (Avs) have paved the way for the arrangement of swivel seats in vehicles, which could pose a challenge to traditional safety systems. The integration of automated emergency braking (AEB) and pre-pretension (PPT) seatbelts improves protection for a vehicle's occupant. The objective of this study is to explore the control strategies of an integrated safety system for swiveled seating orientations. Methods: Occupant restraints were examined in various seating configurations using a single-seat model with a seat-mounted seatbelt. Seat orientation was set at different angles, from -45° to 45° with 15° increments. A pretension was used on the shoulder belt to represent an active belt force cooperating with AEB. A generic full frontal vehicle pulse of 20mph was applied to the sled. The occupant's kinematics response under various integrated safety system control strategies was analyzed by extracting a head pre-crash kinematics envelope. The injury values were calculated for various seating directions with or without an integrated safety system at the collision speed of 20mph. Results: In a lateral movement, the excursions of the dummy head were 100mm and 70mm in the global coordinate system for negative and positive seat orientations, respectively. In the axial movement, the head traveled 150mm and 180mm in the global coordinate system for positive and negative seating directions, respectively. The 3-point seatbelt did not restrain the occupant symmetrically. The occupant experienced greater y-axis excursion and smaller x-axis excursion in the negative seat position. Various integrated safety system control strategies led to significant differences in head movement in the y direction. The integrated safety system reduced the occupant's potential injury risks in different seating positions. When the AEB and PPT were activated, the absolute HIC15, brain injury criteria (BrIC), neck injury (Nij), and chest deflection were reduced in most seating directions. However, the pre-crash increased the injury risks at some seating positions. Conclusion: The pre-pretension seatbelt could reduce the occupant's forward movement in the rotating seat positions in a pre-crash period. The occupant's pre-crash motion envelope was generated, which could be beneficial to future restraint systems and vehicle interior design. The integrated safety system could reduce injuries in different seating orientations.

Design and Fatigue Life Analysis of Air Suspension Z Type Leaf Springs Used in Heavy Commercial Vehicle

Leaf springs are used as suspension elements in the front and rear axles of vehicles in the automotive industry, especially in heavy commercial vehicles. Leaf springs working all road conditions and under load, it provides damping of the loads transmitted from the wheel and from the ground to the chassis. In this way, it ensures the safety and working comfort in the vehicle. Mechanical suspension systems have been replaced by air suspension systems in new generation vehicles. In air suspension systems; Z type parabolic leaf springs, especially used in heavy commercial vehicles, is a type of leaf spring that provides spring back with air bellows. In this study; Z type parabolic leaf spring design and finite element model of this leaf spring were prepared and form and stress analyzes were performed on the model. By using finite element analysis, high-tension regions of this leaf spring were determined and the appropriate design was selected. Finally, the fatigue life of this spring was obtained in the N-Code program and physical test.

A NEW WAY TO CONTROL THE DISTRIBUTION OF BRAKING FORCES BETWEEN THE AXLES OF THE CAR

The distribution of braking forces between the axles has a significant impact on the stability of the car when braking. Over the years, dozens of authors have proposed various methods for regulating braking forces and devices for their implementation (brake force regulators - RTS). The regulation process in most of the known technical solutions was carried out by changing the ratio of the pressure of the working fluid (liquid or air) in the actuators of the brake drive of the front and rear brakes. To change the ratio of the specified pressure, the regulation of the working fluid was used, which led to the instability of the regulation characteristics and the failure of the brake system. In this article, a new method for controlling the distribution of braking forces between the axles is proposed and researched, the essence of which is to change the ratio of the total areas of the actuators of the brake drives (the areas of the pistons of the working brake cylinders). The implementation of this method of controlling the distribution of braking forces between the axles became possible due to the creation of open-type multi-piston disc brakes with two pairs of concentrically arranged brake pads. The proposed method makes it possible to increase the stability of the car not only during emergency, but also during service braking. The aim of the study is to increase the stability of the car during emergency and service braking by using a new method of controlling the distribution of braking forces between the axles by discretely changing the size of the active working areas of the working brake cylinders. To achieve this goal, the tasks of determining the value of the coefficient of distribution of the brake force on the front axle of the car with a different combination of the included working brake cylinders on the wheels and rear wheels and determining the threshold values of the car deceleration, at which it is necessary to change the ratio of the included working brake cylinders on the wheels of the front and rear axles, are solved .The results of the study showed that the use of multi-piston disc brakes on the front and rear wheels of a twoaxle vehicle makes it possible to organize the process of impulse control of the distribution of braking forces between the axles.

The Vehicle Intention Recognition with Vehicle-Following Scene Based on Probabilistic Neural Networks

In the vehicle-following scenario of autonomous driving, the change of driving style in the front vehicle will directly affect the decision on the rear vehicle. In this paper, a strategy based on a probabilistic neural network (PNN) for front vehicle intention recognition is proposed, which enables the rear vehicle to obtain the driving intention of the front vehicle without communication between the two vehicles. First, real vehicle data with different intents are collected and time—frequency domain variables are extracted. Secondly, Principal Component Analysis (PCA) is performed on the variables in order to obtain comprehensive features. Meanwhile, two cases are classified according to whether the front vehicle can transmit data to the rear vehicle. Finally, two recognition models are trained separately according to a PNN algorithm, and the two models obtained from the training are verified separately. When the front vehicle can communicate with the rear vehicle, the recognition accuracy of the corresponding PNN model reaches 96.39% (simulation validation) and 95.08% (real vehicle validation). If it cannot, the recognition accuracy of the corresponding PNN model reaches 78.18% (simulation validation) and 73.74% (real vehicle validation).

Adaptive Cruise Control Based on a Model Predictive Controller Considering the Driving Behavior of the Front and Rear Vehicles

<div>Aiming to improve the lateral instability of adaptive cruise control (ACC) systems, both the front and rear vehicles are considered the centers of two control strategies. A vehicle control system is designed to enable the vehicle to automatically find the best following distance based on the displacement and speeds of the front and rear vehicles, hence enhancing driver assistance, traffic efficiency, and road utilization ratio. A practical model predictive control is designed to improve performance, responsiveness, and minor discomfort. A quadratic programming (QP) solver is used to construct an error preview-based mathematical model for the vehicle control, which is then applied to improve the control performance of the system to achieve relative intervehicle distance control. The time sampling of the parameters and the prediction horizon are obtained by numerical simulation, verifying the effectiveness of the ACC system proposed.</div>

A Car Target Detection Method based on YOLOv7-tiny

If the driver can discover the safety hazards during the driving and brake the vehicle in time, it will effectively reduce the occurrence of traffic accidents and reduce losses. In order to accurately and quickly detect the vehicle in front, this article proposes a car target detection method based on the YOLOv7-tiny algorithm. This method can use the machine vision to detect the vehicle in front. Provide security assistance. The experimental results show that the average accuracy rate of the methods proposed in this article on six common vehicle recognition reached 80.8%, and the model is more lighter.

White Phase Intersection Control Through Distributed Coordination: A Mobile Controller Paradigm in a Mixed Traffic Stream

This study presents a vehicle-level distributed coordination strategy to control a mixed traffic stream of connected automated vehicles (CAVs) and connected human-driven vehicles (CHVs) through signalized intersections. We use CAVs as mobile traffic controllers during a newly introduced “white phase”, during which CAVs will negotiate the right-of-way to lead a group of CHVs while CHVs must follow their immediate front vehicle. The white phase will not be activated under low CAV penetration rates, where vehicles must wait for green signals. We have formulated this problem as a distributed mixed-integer non-linear program and developed a methodology to form an agreement among all vehicles on their trajectories and signal timing parameters. The agreement on trajectories is reached through an iterative process, where CAVs update their trajectory based on shared trajectory of other vehicles to avoid collisions and share their trajectory with other vehicles. Additionally, the agreement on signal timing parameters is formed through a voting process where the most voted feasible signal timing parameters are selected. The numerical experiments indicate that the proposed methodology can efficiently control vehicle movements at signalized intersections under various CAV market shares. The introduced white phase reduces the total delay by 3.2% to 94.06% compared to cooperative trajectory and signal optimization under different CAV market shares in our tests. In addition, our numerical results show that the proposed technique yields reductions in total delay, ranging from 40.2% – 98.9%, compared to those of a fully-actuated signal control obtained from a state-of-practice traffic signal optimization software.

Cooperative vehicle platoon control considering longitudinal and lane-changing dynamics*

This paper presents a distributed cascade Proportional Integral Derivate (DCPID) control algorithm for the connected and automated vehicle (CAV) platoon considering the heterogeneity of CAVs. Furthermore, a real-time cooperative lane-changing model for CAVs, which can seamlessly combine the DCPID algorithm and sine function is developed. The DCPID algorithm determines the appropriate longitudinal acceleration and speed of the lane-changing vehicle considering the speed fluctuations of the front vehicle on the target lane (TFV). In the meantime, the sine function plans a reference trajectory which is further updated in real time using the model predictive control (MPC) to avoid potential collisions. Simulation results indicate that the DCPID algorithm can provide robust control for tracking and adjusting the desired spacing and velocity for all 400 scenarios. Besides, the proposed cooperative lane-changing model can guarantee effective and safe lane changing with different speeds and even in emergency situations.

Development and evaluation of passenger assistance system concepts to reduce passenger discomfort.

The front seat passenger is often neglected when developing support systems for cars. There exist few examples of systems that provide information or interaction possibilities specifically to those passengers. Previous research indicated that the passive role of the passenger can frequently lead to a feeling of discomfort, potentially caused by missing information and missing control with respect to the driving situation. This paper investigates if and how different aspects of cognitive processes as defined in a previously published model can be approached with a technical system to reduce discomfort in passengers. Five prototypical passenger assistance systems are created which provide missing information (for example about the attentiveness of the driver) or the possibility to have more influence as a passenger. In a static simulator study with N = 40 participants, these systems were investigated with respect to their influence on measures of discomfort. Participants experienced in a counterbalanced order car following and braking scenarios on the highway with different time headways (within-subjects), with and without one of the passenger assistance systems (between-subjects). Based on the subjective measures for each experienced situation, three systems were identified as particularly useful in reducing discomfort. These displayed the attentiveness of the driver, the safety distance to a vehicle in front or provided the possibility to signal the driver that the recent safety distance is too small. These best proposals significantly reduced passenger discomfort in the tested Following and Braking scenarios for different time headways. In the post inquiry, more than 64% of the passengers confirmed the helpfulness of the rated system in reducing their discomfort in each case and about 75% of the passengers reported an interest in using it in their vehicle. This demonstrates opportunities to improve the everyday driving experience beyond classical assistance systems by explicitly considering the needs of passengers.

Research on a Cooperative Adaptive Cruise Control (CACC) Algorithm Based on Frenet Frame with Lateral and Longitudinal Directions.

Research on the cooperative adaptive cruise control (CACC) algorithm is primarily concerned with the longitudinal control of straight scenes. In contrast, the lateral control involved in certain traffic scenes such as lane changing or turning has rarely been studied. In this paper, we propose an adaptive cooperative cruise control (CACC) algorithm that is based on the Frenet frame. The algorithm decouples vehicle motion from complex motion in two dimensions to simple motion in one dimension, which can simplify the controller design and improve solution efficiency. First, the vehicle dynamics model is established based on the Frenet frame. Through a projection transformation of the vehicles in the platoon, the movement state of the vehicles is decomposed into the longitudinal direction along the reference trajectory and the lateral direction away from the reference trajectory. The second is the design of the longitudinal control law and the lateral control law. In the longitudinal control, vehicles are guaranteed to track the front vehicle and leader by satisfying the exponential convergence condition, and the tracking weight is balanced by a sigmoid function. Laterally, the nonlinear group dynamics equation is converted to a standard chain equation, and the Lyapunov method is used in the design of the control algorithm to ensure that the vehicles in the platoon follow the reference trajectory. The proposed control algorithm is finally verified through simulation, and validation results prove the effectiveness of the proposed algorithm.

Exploration of the intelligent control system of autonomous vehicles based on edge computing.

The development of science and technology continues to promote the progress of society. The current intelligence and automation technology has become widely used in society. To this end, this study proposes a vehicle intelligent control system based on edge computing and deep learning to promote the far-reaching development of intelligent technology and automation technology. First, control algorithms are used to design a switch control strategy combining accelerator and brake. Second, a fuzzy control algorithm based on vehicle tracking and trajectory deviation is designed to enhance the vehicle's stability during steering. A Convolutional Neural Network (CNN) is used to recognize the car's surroundings as it drives. In addition, accelerator and brake controllers and vehicle tracking and trajectory deviation controllers are connected to the vehicle's wiring. Then, the data transmission function based on edge computing is applied to the vehicle's intelligent control system. Finally, trajectory tracking and emergency braking experiments are carried out on the control system to verify the practicability and reliability of the method and the effectiveness of CNN. The simulation experiments are carried out on two states of medium speed and high speed to verify the effectiveness of the longitudinal anti-collision system of the test vehicle when the target vehicle suddenly decelerates. The results demonstrate that the driving speed of the experimental vehicle is set to 50km/h, the distance between the experimental vehicle and the target vehicle is 40m, and the target vehicle in front drives at a constant speed of 50km/h. The target vehicle in front of the car suddenly decelerates in 5 seconds, and the speed drops to 0 after 5 seconds. The actual distance between the experimental vehicle and the target vehicle is very close to the expected safe space, and the experimental vehicle is in a safe state during this process. When the experimental vehicle starts to decelerate, the experimental vehicle adopts emergency deceleration to ensure a safe distance between the two vehicles. At this time, the car enters the second-level early warning state, but driving safety can still be guaranteed. It is advisable to maintain low-speed emergency braking in this state. This study provides creative research ideas for the follow-up research on the intelligent control system of uncrewed vehicles and contributes to the development of intelligence and automation technology.

Review on Flow Controls for Vehicles Aerodynamic Drag Reduction

Reducing aerodynamic drag in automobiles has been one of the focuses in automotive industry as it will increase vehicles performance and reduce power consumption which subsequently save the environment. Drag is a force that tend to pull the vehicle backwards. Pressure drag is the major contributor to drag of vehicle which caused by lower pressure in the rear area compared to front of vehicle. When vehicle moves forward, the stagnation point occurs in the front part of vehicle. The airflow then goes over the vehicle and separate at the back due to adverse pressure gradient. Thus, flow separation is created in the rear area of vehicle as indicated by the wake. Wake is a low-pressure region that need to be reduced so that the pressure between the front and rear could be reduced to minimize the drag. Flow control is introduced to delay this flow separation and it has been categorized as active and passive. Active flow controls require power input while passive flow controls do not need power input. This review article will explore those studies on flow controls applied in automobiles and investigate their working mechanisms along with research gaps detected. Relative comparison of effectiveness for each flow control is unfeasible as the parameters used differs but this problem can be resolved. Standard parameters need to be used in future researches in order address the discrepancy of results obtained from numerous studies around the globe. A current flow control technique is proposed to be conducted in UTM Aerolab to further investigate this interesting flow field.

One estimation method of road slope and vehicle distance

Aiming at the problem that the intelligent vehicle cannot accurately obtain the vehicle distance information when the front and rear vehicles are located on the road surface with different slopes, this paper proposes a road slope and vehicle distance estimation method based on vision’s and lidar’s information fusion. Firstly, the road lane line is detected by combining vision detection and lidar positioning ability, and the slope of the lane line is calculated. Then, we judge and analyze the slope ahead according to the change of the lane line slope, estimate the slope value according to the vanishing point coordinates. Finally, an improved distance estimation method adapted to the conditions of horizontal road surface and slope road surface is designed based on the combination of Lidar and camera. The experimental results show that the improved distance estimation model has good performance and can reach the accuracy of more than 95%.

A deep learning approach for vehicle velocity prediction considering the influence factors of multiple lanes

<abstract> <p>Predicting the future velocity of vehicles is essential for the safety of autonomous driving and the Intelligent Transport System. This study investigates how the surrounding vehicles influence a driving vehicle. Based on the HighD dataset, a scenario that considers the current lane and the neighboring lanes is selected while the drivers' visual angles and visual gap angles along with other parameters in the dataset are characterized as features. To predict the velocity of a driving vehicle and calibrate the influence of surrounding vehicles, a Transformer-based model integrating the features of multiple vehicles is proposed, and different features are added to the layers while constructing the model. Moreover, the information from previous timestamps of the vehicle state is integrated to estimate the duration of the influences, since the influence of an incident is not instantaneous. In our experiments, we find that the duration of the influence on the driving state perfectly fits the driver's reaction time when maneuvers occur in the surrounding vehicles. In addition, we further quantify the importance of the influence on the vehicle velocity prediction based on the Random Forest and obtain some practical conclusions, for instance, the velocity of a vehicle is more influenced by the front vehicle in the left lane than that in the right lane, but is still mainly influenced by the front vehicle in the current lane.</p> </abstract>

Performance Evaluation of Energy-Optimal Adaptive Cruise Control in Simulation and on a Test Track

This paper presents a comprehensive evaluation of energy-optimal adaptive cruise control (EACC) based on model predictive control (MPC) in comparison to standard adaptive cruise control (ACC) which is equipped for mass-produced cars of a German car maker. First, a simulation study is made in this work to compare the performance of PID-based control and MPC. Afterwards, a prototype of EACC is installed in an electric vehicle (EV), which is tested as the host car and follows a preceding car on a test track. In the test cycles, which represent urban and rural driving scenarios, EACC reduces the energy consumption of the host car by up to 13% in comparison with the standard ACC. The results of the vehicle tests not only demonstrate a significant energy reduction by EACC, but also show that EACC can reduce the risk of rear-end collisions by reacting earlier to rapid deceleration of the vehicle in front. Furthermore, the passengers feel more comfortable as the vehicle speed trajectory is smoothened by EACC. Finally, to investigate the real-time implementation capability of the optimization algorithms, this paper also examines the computation time of the MPC-based EACC function with different lengths of the MPC horizon.

Leading Pedestrian Intervals at Urban Crosswalks: Effects on Safety for Travelers Who Are Blind

Introduction: The use of leading pedestrian intervals (LPIs) creates safer street crossings for typical pedestrians, giving them a head-start into the crosswalk. The LPI, however, delays the onset of the traditional traffic sound cue that pedestrians who are blind use to initiate crossing streets. LPIs can create situations where pedestrians who are blind will frequently begin or end crossings or both against the pedestrian signal. Methods: The researchers conducted two sets of observations in an urban environment. They measured the seconds from the initiation of the vehicular circular green signal to the front of the first vehicle passing through the center of the intersection (n = 40) to calculate the average driver's delay. Pedestrians who are blind, to confirm a straight-through surge of parallel traffic (i.e. not turning vehicles), listen for a vehicle to reach the roadway center to know it is the appropriate time to initiate a crossing. In the second set of observations (n = 40), the researchers recorded the length of the Walk, Flashing Don’t Walk, and LPI to calculate average impacts on crossing. Results: The average time for vehicles to reach the center of the intersection was 6.0 s. When there was an LPI, on average the clearance for pedestrians after reaching the far corner was 0.49 s; for Walk intervals, there was an average of 3.35 s margin of time to begin crossing. Discussion: These observational studies verify the crossing latencies for pedestrians who are blind. At crosswalks with LPI, pedestrians can be expected to cross after the Walk sign has expired 15 to 38% of the time and end their crossing 40 to 82% of the time when the visual display says Don’t Walk. Implications for Practitioners: Orientation and mobility specialists should instruct students on how to identify intersections with LPI to assist in decision making. Traffic management remediation is the installation of an accessible pedestrian signal (APS); specialists should advocate for the installation of APS where LPIs are implemented and teach students how to use APS.

Design of Reward Function on Reinforcement Learning for Automated Driving

This paper proposes a design scheme of reward function that constantly evaluates both driving states and actions for applying reinforcement learning to automated driving. In the field of reinforcement learning, reward functions often evaluate whether the goal is achieved by assigning values such as +1 for success and -1 for failure. This type of reward function can potentially obtain a policy that achieves the goal, but the process by which the goal is reached is not evaluated. However, process to reach a destination is important for automated driving, such as keeping velocity, avoiding risk, retaining distance from other cars, keeping comfortable for passengers. Therefore, the reward function designed by the proposed scheme is suited for automated driving by evaluating driving process. The effects of the proposed scheme are demonstrated on simulated circuit driving and highway cruising. Asynchronous Advantage Actor-Critic is used, and models are trained under some situations for generalization. The result shows that appropriate driving positions are obtained, such as traveling on the inside of corners, and rapid deceleration to turn along sharp curves. In highway cruising, the ego vehicle becomes able to change lane in an environment where there are other vehicles with suitable deceleration to avoid catching up to a front vehicle, and acceleration so that a rear vehicle does not catch up to the ego vehicle.

Analysis of the Influence of the Front Vehicle on the Propagation Loss of ETC System of the Back Vehicle

Analysis of the Influence of the Front Vehicle on the Propagation Loss of ETC System of the Back Vehicle

Composition of Automated Vehicle Groups with Control Barrier and Lyapunov Functions Using Slope Information

In recent years, the rapid increase in the demand for automobiles has caused serious traffic congestion, with the two major contributing factors being highway tunnels and sag sections. In sag sections, the velocity of the front vehicle is reduced because of its inability to recognize the uphill slope, and this reduction in velocity is amplified and propagated to the following vehicles, resulting in traffic congestion. In this study, control barrier functions (CBFs) and control Lyapunov functions (CLFs) are utilized in adaptive cruise control systems to account for multiple constraints in nonlinear systems. When using CBFs and CLFs as constraint in sag sections, we proposes a method to resolve vehicle group instability with congestion resolution in consideration by using the slope information for control conditions to make CBFs and CLFs usable in a multitude of situations.