Search Results

Intelligent vehicle-to-everything connectivity is an important development trend in the automotive industry. Among various active safety systems, Autonomous Emergency Braking (AEB) has garnered widespread attention due to its outstanding performance in reducing traffic accidents. AEB effectively avoids or mitigates vehicle collisions through automatic braking, making it a crucial technology in autonomous driving. However, the majority of current AEB safety models exhibit limitations in braking modes and fail to fully consider the overall vehicle stability during braking. To address these issues, this paper proposes an improved AEB control system based on a risk factor (AERF). The upper-level controller introduces the risk factor (RF) and proposes a multi-stage warning/braking control strategy based on preceding vehicle dynamic characteristics, while also calculating the desired acceleration.

As the automotive industry accelerates its virtual engineering capabilities, there is a growing requirement for increased accuracy across a broad range of vehicle simulations. Regarding control system development, utilizing vehicle simulations to conduct ‘pre-tuning’ activities can significantly reduce time and costs. However, achieving an accurate prediction of, e.g., stopping distance, requires accurate tire modeling. The Magic Formula tire model is often used to effectively model the tire response within vehicle dynamics simulations. However, such models often: i) represent the tire driving on sandpaper; and ii) do not accurately capture the transient response over a wide slip range. In this paper, a novel methodology is developed using the MF-Tyre/MF-Swift tire model to enhance the accuracy of ABS braking simulations.

The aim of this study is to determine if the degradation of one or more dampers of a passenger car with ABS leads to a statistically significant reduction of vehicle safety. Therefore, a compact and a mid-size car are tested on a flat test track and on an uneven test track by straight braking maneuvers at different levels of damper degradation. Both test tracks are scanned using a 3D laser scanner. For every level of damper degradation (on each test track) a new set of tires is used, a preconditioning routine is applied and 30 successful measurements are conducted to allow using statistical methods to evaluate the results. The results show that any level of damper degradation with each type of car and test track leads to a significant increase in braking distance and, therefore, to a significant reduction of vehicle safety. The braking distance extension varies heavily with the level of damper degradation and the road properties.

Disc brakes are the most popular type of brakes used in the two-wheeler segment and are easily available in the market. The improper brakes result in serious problems in vehicles. The main idea of this paper is to design a braking system for a two-wheeler application. The paper discusses the design, analysis, and simulation of disc brakes. The disc is first selected using the standard brake disc calculation. To verify the selection of disk, torque at wheel and torque at the disc are compared. Thermomechanical (Transient) analysis is done on ANSYS 2021 to check for the effect of braking force applied by the disc on the rotor disc. The mathematical model of the ABS model is done on Scilab Xcos. The main aim of studying the system using a mathematical model is to verify if the selected disc brakes are safe enough to be installed on a two-wheeler. The mathematical model also has stopping distance and the stopping time as the output which validates the selection of the disc.

From the past few years, there is a pressing need for implementation of automatic in-vehicle safety systems to avoid vehicle crashes and fatalities. Development of autonomous emergency braking systems (AEBS) to detect and avoid collisions in such critical moments is of paramount importance. In this paper, AEBS is developed for a four-wheeler system that aims to detect vehicles and controls the ego vehicle based on the expected stooping distance (ESD). This control system aims to react based on the real-time relative distance & speed of the ego vehicle to actuate appropriate braking force. Control systems developed in Altair Activate are co-simulated with CARLA, a virtual reality simulator for autonomous driving research. Various scenarios including low and high-speed car to car motion, urban high and low traffic density environments are simulated to study the robustness of the control system.

India is one of the largest markets for the automobile sector and considering the trends of road fatalities and injuries related to road accidents, it is pertinent to continuously review the safety regulations and introduce standards which promise enhanced safety. With this objective, various Advanced Driver Assistance Systems (ADAS) regulations are proposed to be introduced in the Indian market. ADAS such as, Anti-lock Braking Systems, Advanced Emergency Braking systems, Lane Departure Warning Systems, Auto Lane Correction Systems, Driver Drowsiness Monitoring Systems, etc., assist the driver during driving. They tend to reduce road accidents and related fatalities by their advanced and artificial intelligent fed programs. This paper will share an insight on the past, recent trends and the upcoming developments in the regulation domain with respect to safety.

An Inertial Measurement Unit (IMU) provides vehicle acceleration that can be used in Active Vehicle Safety Systems (AVSSs). However, the signal output from an IMU is affected by changes in its position in the vehicle and alignment, which may lead to degradation in AVSS performance. Investigators have employed physics and data-based models for countering the impact of sensor misalignment, and the effects of gravity on acceleration measurements. While physics-based methods utilize parameters varying dynamically with vehicle motion, data-based methods require an extensive number of parameters making them computationally expensive. These factors make the above-explored methods practically challenging to implement on production vehicles. This study considers a 6-axis IMU and evaluates its impact on Antilock Braking System (ABS) performance by considering the IMU signal obtained with different mounting orientations, and positions on a Heavy Commercial Road Vehicle (HCRV).

An accurate estimate of vehicle speed is essential for optimal anti-lock braking system (ABS) calculations. Currently, most vehicles including heavy-duty class 8 trucks mainly rely on wheel speed sensors (WSS) to estimate velocity. However, as soon as braking is applied, WSS become inaccurate for determining the velocity due to the longitudinal slip developed in the tires. Using the inertial measurement unit (IMU) to estimate vehicle speed allows for its use in conjunction with the WSS to accurately calculate the slip ratio at each tire. These slip ratio values can then be used as the main control variable in the ABS algorithm to utilize the grip available more fully at each tire, to improve stopping distance and controllability. A steady state braking analysis model is developed and validated against Federal Motor Vehicle Safety Standards (FMVSS) 121 60-0 mph stopping distance data for a loaded class 8 tractor semi-trailer combination.

The use of personal light electric vehicles (PLEVs), such as electric scooters, has rapidly increased in recent years. However, their widespread use has raised concerns about rider safety due to their vulnerability in shared traffic spaces. To address this issue, this paper presents a radar-based rider assistance system aimed at enhancing the safety of PLEV riders. The system consists of an adaptive feedback system and a single-channel anti-lock braking system (ABS). The adaptive feedback system uses multiple-input multiple-output (MIMO) radar sensors to detect nearby objects and provide real-time warnings to the rider through haptic, visual, and acoustic signals. The system takes into account traffic density and uses online data to warn about obscured objects, thereby improving the rider’s situational awareness. Results from testing the feedback system show that it effectively detects potential collisions and provides warning signals, reducing the risk of accidents.

Recently, there has been a slight increase in interest in the use of responder-to-vehicle (R2V) technology for emergency vehicles, such as ambulances, fire trucks, and police cars. R2V technology allows for the exchange of information between different types of responder vehicles, including connected and automated vehicles (CAVs). It can be used in collision avoidance or emergency situations involving CAV responder vehicles. The benefits of R2V are not limited to fully autonomous vehicles (e.g., SAE Level 4), but can also be used in Level 2 CAV scenarios. However, despite the potential benefits of R2V, discussions on this topic are still limited. Responder-to-Vehicle Technologies for Connected and Autonomous Vehicles aims to provide an overview of R2V technology and its applications for CAV systems, particularly in the context of collision avoidance features. The responder vehicles in question can be autonomous or non-autonomous.

This paper describes a Hardware-In-the-Loop (HIL) platform based on the dual-axis dynamometer for development and validation of ABS/TCS controllers. Antilock Braking System (ABS) and Traction Control System (TCS) are standard equipment for passenger vehicles. The ABS, an anti-skid braking assistance system, promotes safety by preventing the locking of wheels during braking. TCS is a control system that prevents the wheels from slipping by moderating driving power to the one that is losing its grip on the road. The real-time platform is based on a dSPACE vehicle model and the simulation environment, and it consists of an actual drive motor, hydraulic braking system and Chroma dual-axis dynamometer test bench, which provide more realistic and complicated conditions than the one-axis platform. With dual-axis architecture, it could effectively perform simulation results of model on two axes.

The interaction between driveline control and anti-lock braking system (ABS) control in electric vehicles (EV) was investigated based on multi-body dynamics (MBD) model and control model co-simulation. Two primary driveline control algorithms, active damping control and wheel flare control, were integrated with ABS control in Simulink model and the influence on ABS control was studied. The event for high mu to low mu transition was simulated. When ABS control is active on low mu surface, the vehicle shows large wheel slip and long duration time before wheel speed returns to stable control. This performance could be improved with activating driveline control. Deceleration uniformity metric shows that active damping control has very small effect when ABS control becomes stable after passing through the high mu to low mu transition period. Driveline damping control can help to reduce vibration, but it is difficult to find satisfied tuning for wheel speed performance.

Autonomous truck with modular chassis has the characteristics of high driving flexibility and strong load capacity. It can be equipped with different numbers of modular chassis according to the task requirements. The application of autonomous truck can solve the problems of traffic accidents and shortage of drivers effectively, which is the development trend of trucks in the future. For the collision-free trajectory planning problem of dual-modular chassis autonomous truck, this paper designs a hierarchical local trajectory planner that combines the artificial potential field method with polynomial curve fitting method. This planner plans the center of mass trajectory firstly, and then generates the modular chassis trajectories according to the position relationship between the center of mass and the chassis.

The Anti-Lock Braking System (ABS) is a safety critical feature primarily used to control slipping of wheels, to maximize available traction and minimize stopping distance. Regulatory authorities of many countries have mandated implementation of an ABS as a compulsory safety feature to be present in all road legal automobiles. Hence, apart from avoiding wheel lock-up, an ABS must also ensure that the vehicle maintains its handling stability and steerability while braking. Thus, it is important that the ABS controller modulate and apply adequate amount of brake cylinder pressure. This paper suggests the use of a Tire Force based algorithm to analyze vehicle behavior and accordingly a control law is employed to modulate the wheel brake pressure.

The anti-lock brake system (ABS) is a vital system in modern vehicles that prevents automotive wheels from locking during an emergency brake. This paper aims to introduce an efficient, optimized proportional integral derivative (PID) controller tuned using a genetic algorithm (GA) to enhance the performance of ABS. The PID control method is a very famous control algorithm employed in numerous engineering applications. The GA is used to solve the nonlinear optimization problem and search for the optimum PID controller gains by identifying the solution to the problem. A mathematical model of ABS is derived and simulated using Matlab and Simulink software. The proposed optimized PID-controlled ABS is compared to the conventional ABS controlled using a Bang-Bang controller. System performance criteria are evaluated and assessed under different road adhesion coefficient values to judge the success of the proposed PID controller tuned using GA.

The towbarless aircraft taxiing system (TLATS) consists of the towbarless towing vehicle (TLTV) and the aircraft. The tractor realizes the towing work by fixing the nose wheel. During the towing process, the tractor driver may cause the aircraft to collide with an obstacle because of the blind spot of vision leading to the accident. The special characteristics of aircraft do not allow us to modify the structure of the aircraft to achieve collision avoidance. In this paper, three degrees of freedom (DOE) kinematic model of the tractor system is established for each of the two cases of pushing and pulling the aircraft, and the relationship between the coordinates of each danger point and the relatively articulated angle of the TLATS and the velocity of the midpoint of the rear axle is derived.

Anti-lock brake systems (ABS) produce high levels of vehicle deceleration under emergency braking conditions by modulating tire slip. Currently there are limited data available to quantify the mean, variance, and distribution of vehicle deceleration levels for modern ABS-equipped vehicles. We conducted braking tests using twenty (20) late-model vehicles on contiguous dry asphalt and concrete road surfaces. All vehicles were equipped with a 5th wheel sampled at 200 Hz, from which vehicle speed and deceleration as a function of time were calculated. Eighteen (18) tests were conducted for each vehicle and all tests were conducted from a targeted initial speed of 65 km/h (40 mph). Overall, we found that late-model ABS-equipped vehicles can decelerate at average levels that vary from about 0.871g to 1.081g across both surfaces, and that deceleration levels were on average about 0.042g higher on asphalt than on concrete.

In this study, we introduce an electronically controlled brake system (ECB) that can be applied to electric vehicles (EVs) and internal combustion engine vehicles (ICEVs). The main features of the ECB include maximizing the regenerative energy while maintaining vehicle stability and ensuring redundancy in automatic braking. The brake system consists of upper and lower units. The newly developed upper unit has a brake-by-wire configuration and can control the front and rear wheel pressures separately. Hereinafter, controlling the front and rear wheel pressures separately is referred to as two-channel pressure control. The regenerated energy can be maximized while appropriately maintaining the distribution of the front and rear braking forces based on the two-channel pressure control during regenerative cooperation. The lower unit is a conventional hydraulic unit for executing anti-lock brake control, electronic stability control and so on.

To avoid pedestrian fatalities due to factors such as understeer of the vehicle or negligence in steering by the driver, a step further needs to be taken to improve the steering kinematics and have object detection integration for a robust solution. Here we integrate human detection concepts in computer vision with steering dynamics concepts in vehicle kinematics to give the framework that achieves the ability to avoid pedestrian fatalities due to its ability, innovativeness, and integrated units. The paper consists of a description of each component and the overall system function. Results of the pedestrian sensing system and steering kinematics have been included in this document in the form of program outputs, simulation results, and calculations.

In the intelligent transportation roadside object detection scene, due to the limited computing power of edge computing equipment, it is difficult to deploy large and complex object detection models, at the same time, most of detection models can not give consideration to precision and real-time. The complex road environment requires the model to have higher detection precision and faster detection speed. To solve this problem, an improved lightweight object detection model based on YOLOv4 is proposed(STDC_YOLO). The proposed model uses short-time dense network structure (STDC) to replace cross stage partial model structure (CSP), employs multi-scale feature fusion module which combines low-level feature and high-level feature to enrich feature information. In addition, the proposed STDC_YOLO employs re-parameterized VGG block (RepVGG) to improved parameter efficiency, thereby improving the inference speed.