Emergency Braking Control Research Articles

Research on a Hierarchical Control Strategy for Anti-Lock Braking Systems Based on Active Disturbance Rejection Control (ADRC)

To improve the slip rate control effect for different road conditions during emergency braking of wheel hub motor vehicles, as well as to address the problems of uncertainty and nonlinearity of the system when the electro-mechanical braking system is used as the actuator of the ABS, a hierarchical control strategy of the anti-lock braking system (ABS) using active disturbance rejection control (ADRC) is proposed. Firstly, a vehicle dynamics model and an ABS model based on the EMB system are established; secondly, a speed observer based on the dilated state observer is used in the upper layer to design a pavement recognition algorithm, which recognizes the current pavement and outputs the optimal slip rate; then, an ABS controller based on the ADRC algorithm is designed for the lower layer to track the optimal slip rate. In order to verify the performance of the pavement recognition method and control strategy, vehicle simulation software is used to establish the model and simulation. The results show that the road surface recognition method can quickly and effectively recognize the road surface, and comparing the emergency braking control effects of PID and SMC under different road surface conditions, the ADRC strategy has better robustness and reliability, and improves the braking effect.

Research on an Automatic Emergency Braking Control Method Based on Coordinated Wheel Deflection

Research on an Automatic Emergency Braking Control Method Based on Coordinated Wheel Deflection

ADAS and automation-arelevant contribution to maintaining mobility of older drivers?

Driving is the most important and safest form of mobility for the majority of senior citizens. However, physical and mental performance gradually decline with age, which can lead to more problems, critical situations or even accidents. Vehicle technology innovations such as advanced driver assistance systems (ADAS) have the potential to increase the road safety of older people and maintain their individual mobility for as long as possible.This overview article aims to identify ADAS that have the greatest potential to reduce the number of accidents involving older drivers. For this purpose, the accident and damage occurrence as well as the driving behaviour and compensation strategies of older people are examined in more detail. Suitable ADAS should compensate for typical driver errors, reduce information deficiencies and have ahigh level of acceptance. For older drivers, emergency braking, parking assistance, navigation, intersection assistance and distance speed control systems as well as systems for detecting blind spots and obstacles appear to be particularly suitable.Some of the disadvantages of ADAS are the lack of market penetration, acceptance problems and interface designs that have not yet been optimally adapted to the needs of older users. For older drivers in particular, it appears to be apriority to develop coherent and integrated solutions in the sense of cooperative assistance instead of pushing ahead with high and full automation with many system limits and exceptions, which can place high demands on attention, for example if the vehicle has to be taken over in acritical situation.

Enhancing Reliability of Advanced Driver-Assistance Systems through Predictive Maintenance and Data-Driven Insights

The advancement of Advanced Driver Assistance Systems (ADAS) marks a pivotal evolution in automotive technology, aiming to enhance road safety and driving efficiency through a wide array of functionalities like blind spot detection, emergency braking, and adaptive cruise control. This research paper delves into the operational integrity, performance metrics, and maintenance strategies of ADAS components, underpinned by a comprehensive methodology involving data collection, pre-processing, feature engineering, machine learning model development, and rigorous validation processes. Systematic inspection of ADAS components indicates their importance in vehicle safety and reliability. The visibility, distance, speed, and steering angle of front cameras, LiDAR, radar, and ultrasonic sensors are carefully evaluated. Maintenance logs show proactive error code management, boosting efficiency. SVM, Gradient Boosting, and Random Forest machine learning models predicted ADAS component failures during validation and testing. Random Forest scored 90% accuracy, 92% precision, 88% recall, and 90% F1. Gradient Boosting was the most accurate, with 93% accuracy, 94% precision, 91% recall, and 92% F1. SVM predicted ADAS component failures with 88% accuracy, 90% precision, 85% recall, and 87% F1 score. Machine learning helps shift from reactive to proactive maintenance. Modelling sensor signal quality, actuator reaction times, error code frequencies, and maintenance intervals enables predictive maintenance and failure detection. Feature engineering builds predictive models using maintenance logs and operational KPIs. The models predict ADAS component failures, boosting vehicle safety and dependability. Using external data improves predictive maintenance models. The maintenance model's adaptability and forecast accuracy are proved by ADAS operation after traffic, accident, and manufacturer upgrades. Predictive maintenance and machine learning improve ADAS dependability and safety, the study found. Advanced analytics and data-driven insights can reduce automotive system failures, improving safety and reliability.

Study on longitudinal dynamics of 5000t heavy haul train on mountain railway

In order to analyze the feasibility of running 5000t heavy-haul trains on a railway in a mountainous area, a longitudinal dynamic model of heavy-haul trains is established, taking full account of the actual horizontal profile of the railway line in the mountainous area. The results of the model are compared and analyzed with those of China Academy of Railway Sciences through a numerical example, and the results are in good agreement, which verifies the accuracy of the model. Based on this model, the longitudinal dynamic performance of planned running marshalling under the conditions of ramp traction, restricted ramp braking, emergency braking and resistance speed control braking is analyzed. The results show that the longitudinal impulse of the marshalling under each condition has a large margin compared with the safety standard. The study demonstrates the feasibility of 5000t heavy-haul train operation in the mountainous area from the perspective of simulation, and provides a theoretical basis for the safe operation of 5000t railway in the mountainous area.

Research on high-speed railway vehicle emergency braking control based on rail surface foreign matter identification

In order to improve the operation stability of high-speed railway vehicles, an emergency braking control method for high-speed railway vehicles based on the identification of foreign matters on the track surface is proposed. The dynamic frame scanning method is used to build the image acquisition of abnormal objects on the high-speed rail track surface, and the visual sensor and vibration sensor are used to identify and process the features of foreign objects on the high-speed rail track surface. According to the abnormal feature extraction results, the command signal in case of foreign objects is transmitted to the terminal command library of the braking control system. According to the transfer switch of the emergency braking system to display the main emergency disposal output states such as the train emergency disposal information reception, emergency disposal level, emergency disposal type and other information, the joint control method of contact pressure distribution and non-uniform heat flux is adopted, and the joint control method of control system, drive system, disc friction system and signal acquisition system is adopted. The emergency braking control module is established to realize the emergency braking control of high-speed railway vehicles according to the feature recognition results of foreign matters on the rail surface. The test results show that the control module is less affected by the vehicle wheelbase characteristics, track structure characteristics and other factors when using this method for high-speed railway vehicle emergency braking control, and has a strong ability to identify the characteristics of foreign matters on the track surface, which improves the stability and real-time performance of emergency braking.

When and How to Apply Automatic Emergency Brakes Based on Risk Perception and Professional Driver Emergency Braking Behavior

<div>The key issues of automatic emergency braking (AEB) control algorithm are when and how to brake. This article proposes an AEB control algorithm that integrates risk perception (RP) and emergency braking characteristics of professional drivers for rear-end collision avoidance. Using the formulated RP by time to collision (TTC) and time headway (THW), the brake trigger time can be determined. Based on the professional driver fitting (PDF) characteristic, the brake pattern can be developed. Through MATLAB/Simulink simulation platform, the European New Car Assessment Programme (Euro-NCAP) test scenarios are used to verify the proposed control algorithm. The simulation results show that compared with the TTC control algorithm, PDF control algorithm, and the integrated PDF and TTC control algorithm, the proposed integrated PDF and RP control algorithm has the best performance, which can not only ensure safety and brake comfort, but also improve the road resource utilization rate.</div>

Automatic Emergency Collision Avoidance of Straight-Crossing Intelligent Vehicle at a 90-Degree Intersection Based on Vehicle-to-Everything Technology

<div>In order to reduce collision at a 90-degree intersection, an automatic emergency collision avoidance control method for intelligent vehicles based on vehicle-to-everything (V2X) technology is proposed. Most of the existing automatic emergency braking (AEB) control algorithms are designed for a single high-friction road with reference to the European New Car Assessment Programme (Euro NCAP) evaluation procedures, and they do not consider changes in road friction. Thus, it may be difficult to avoid collision successfully on a low-friction road. Although some studies have considered the variation of road friction, they are only applicable to straight-line rear-end collisions and cannot be directly applied to intersections. In addition, most studies regard the vehicle only as a particle, ignoring the actual dynamic characteristics of the vehicle. The main contribution of this article is to present an AEB control strategy by V2X technology, which can make the intelligent vehicle avoid collisions at a 90-degree intersection effectively. The proposed time-to-collision (TTC) adaptive algorithm has considered various road surfaces, and its effectiveness is verified by the co-simulation of Matlab/Simulink, CarSim, and Prescan on a typical urban intersection road.</div>

A Rapid Verification System for Automatic Emergency Braking Control Algorithm of Passenger Car

The automatic emergency braking (AEB) system of the passenger car is responsible for auxiliary braking judgment and decision-making in an emergency. Due to the inevitable pressure response delay of passenger car pneumatic braking systems, a large number of verification tests should be carried out to propose appropriate strategies and algorithms. To realize the rapid verification of the AEB control algorithm, a verification system integrating software-in-the-loop (SIL) and hardware-in-the-loop (HIL) was proposed for a two-axle passenger car. It can verify the logic feasibility of the control algorithm through SIL testing, and can verify the implementation effect of the control algorithm through HIL testing. The verification system is composed of IPG, dSPACE, and a pneumatic braking bench. Considering the influence of pneumatic braking delay, it is well-matched with the actual vehicle AEB system. The AEB hierarchical control algorithm was verified under three typical test conditions. The results show that the SIL testing results of speed and relative distance are in good agreement with the HIL testing results, and the average relative deviation of relative distance is only 1.7 m. The single test time of the SIL testing is about 228 s, which can meet the requirements of rapid verification of the AEB control algorithm of the passenger car.

Characterizing future crashes on Indian roads using counterfactual simulations of pre-crash vehicle safety technologies

India's national road crash statistics indicate a continuing increase in casualties. Pre-crash safety technologies are effective in high-income countries, but it is unclear how these will perform in India and which crash types will remain after their implementation. The study objective was to predict and characterize the crashes resulting in moderate or more-severe injuries (Maximum Abbreviated Injury Scale 2 or above: MAIS2+) that remain on Indian roads after 22 pre-crash safety technologies have been implemented in all cars, heavy vehicles (buses and trucks), and Powered Two-Wheelers (PTW). Two deterministic rulesets (one optimistic and one conservative) were modeled for each of the pre-crash safety technologies. Each rule was designed and tuned to the functionality of one technology. The data were obtained from the Road Accident Sampling System India (RASSI) database. In addition to the effectiveness of each technology alone, the combined effectiveness of all technologies was estimated. Further, the characteristics of those crashes that none of the technologies would have avoided were determined. Rear-end-specific Autonomous Emergency Braking (AEB REAR-END) and Electronic Stability Control (ESC) installed in cars and heavy vehicles reduced MAIS2+ crashes the most. Crashes between PTWs and cars were significantly reduced by a rear-end-specific AEB installed in the cars. A pedestrian-specific AEB (AEB-PED) in cars and heavy vehicles was also shown to be effective. The only pre-crash safety technology in PTWs that was included, Antilock Braking Systems (ABS), reduced overall PTW crash involvement, but only reduced PTW-to-pedestrian crashes marginally. The largest proportion of remaining crashes were those that involved PTWs, indicating that PTW safety will remain a concern in future.

A Study on Automatic Emergency Braking Control Algorithm Based on Professional Drivers’ Braking Behavior

A Study on Automatic Emergency Braking Control Algorithm Based on Professional Drivers’ Braking Behavior

The safety potential of automatic emergency braking and adaptive cruise control and actions to improve the potential

The study investigates the potential of Automatic Emergency Braking (AEB) and Adaptive Cruise Control (ACC) systems to prevent fatal rear-end, intersection and pedestrian crashes in Finland. The sy...

The safety potential of automatic emergency braking and adaptive cruise control and actions to improve the potential

The safety potential of automatic emergency braking and adaptive cruise control and actions to improve the potential

Relevance analysis of AEB control strategy and occupant kinematics based on typical cut-in scenario

The objective of this study was to explore the relevance between Autonomous Emergency Braking (AEB) control strategy and occupant pre-impact kinematics among a typical real-world cut-in impact scenario. First, the accident scenario was built with PreScan software after accident analysis. Second, a MADYMO simulation model with Active Human Model (AHM) was built and validated with the volunteer test carried out by our team. Finally, the AEB module and related control strategies were introduced into the main vehicle, and the effects of different strategies on the occupant kinematic were evaluated. The simulation results indicated that it was efficient to evaluate the occupant kinematics during pre-impact phase through vehicle and occupant integrated simulation method. The main vehicle’s velocity could be reduced between 5 km/h and 14 km/h respectively after introducing different AEB control strategies, which was less than the one manoeuvred by driver (22 km/h). Earlier activation of the AEB and heavier braking could result in larger up-body displacement, but less final impact velocity, and the maximum head displacement reached 172.56 mm due to the AEB control. Comparing partial braking with detection angle 9° case with 100% braking with detection angle 18° case, the head, thorax and shoulder displacements were increased by 94.8%, 104.1%, and 48.7%. This research is beneficial for the subsequent integrated safety analysis.

Emergency braking control strategy for high speed electric drive tracked vehicle

Emergency braking control strategy for high speed electric drive tracked vehicle

Automated hazard escaping trajectory planning/tracking control framework for vehicles subject to tire blowout on expressway

This paper proposes an automated hazard escaping trajectory planning/tracking control framework for the vehicle subject to tire blowout on expressway by hierarchically introducing detector, planner, controller. Firstly, a detector is presented for seeking the hazard escaping opportunity by collecting and processing the information from onboard sensor, vehicle-to-vehicle and vehicle-to-infrastructure communication. Secondly, a time-based polynomial trajectory planning method is developed for achieving the smoothly transition from current lane to the emergency one, by which the vehicle velocity and dynamics constraints are both readily handled. Thirdly, an emergency braking control strategy on destination lane is designed by coordinating the primary and auxiliary brake wheels, in which the efficient lane keeping and braking performance are assured at the same time. In the end, combining with the layered trajectory tracking controllers, simulation with front-left wheel tire blowout case validates the feasibility and effectiveness of the proposed framework and methods.

A Novel Emergency Braking Control Strategy for Dual-Motor Electric Drive Tracked Vehicles Based on Regenerative Braking

Dual-motor electric drive tracked vehicles (DDTVs) have drawn much attention in the trends of hybridization and electrification for tracked vehicles. Their transmission chains differ significantly from the traditional ones. Due to the complication and slug of a traditional tracked vehicle braking system, as well as the difference of track-ground with tire-road, research of antilock braking control of tracked vehicles is rather lacking. With the application of permanent magnet synchronous motors (PMSMs), applying an advanced braking control strategy becomes practical. This paper develops a novel emergency braking control strategy using a sliding mode slip ratio controller and a rule-based braking torque allocating method. Simulations are conducted under various track-ground conditions for comparing the control performance of the proposed strategy with three other strategies including the full braking strategy, traditional antilock braking strategy, as well as sliding mode slip ratio strategy without the use of motors. For an initial speed of 80 km/h, simulation results show that the proposed control strategy performs the best among all strategies mentioned above. Several hardware-in-the-loop (HIL) experiments are conducted under the same track-ground conditions as the ones in the simulations. The experiment results verified the validity of the proposed emergency braking control strategy.

PHD filter for vehicle tracking based on a monocular camera

Abstract Novel advance driver assistance systems, such as emergency braking and adaptive cruise control require the most reliable detection algorithms. Furthermore, in the recent years, the use of computer vision approaches in these type of applications is becoming more frequent. However, when dealing with these technologies, reliability is a very important factor that still requires improvement. On this paper, it is presented a tracking algorithm which aims in improving the accuracy of these applications, based on computer vision and modern Probability Hypothesis Density (PHD) Filter technique. The tracking is performed on the features detected within the bounding box provided by a computer video based vehicle detection algorithm. The features tracked are combined in a last stage, providing accurate monocular camera tracking. Test provided, allowed to identify the best method for feature combination. Furthermore, it was proved that under the proper visibility conditions, the PHD filter design is able to improve current methods such as Unscented Kalman Filter.

Advanced Emergency Braking Control Based on a Nonlinear Model Predictive Algorithm for Intelligent Vehicles

Focusing on safety, comfort and with an overall aim of the comprehensive improvement of a vision-based intelligent vehicle, a novel Advanced Emergency Braking System (AEBS) is proposed based on Nonlinear Model Predictive Algorithm. Considering the nonlinearities of vehicle dynamics, a vision-based longitudinal vehicle dynamics model is established. On account of the nonlinear coupling characteristics of the driver, surroundings, and vehicle itself, a hierarchical control structure is proposed to decouple and coordinate the system. To avoid or reduce the collision risk between the intelligent vehicle and collision objects, a coordinated cost function of tracking safety, comfort, and fuel economy is formulated. Based on the terminal constraints of stable tracking, a multi-objective optimization controller is proposed using the theory of non-linear model predictive control. To quickly and precisely track control target in a finite time, an electronic brake controller for AEBS is designed based on the Nonsingular Fast Terminal Sliding Mode (NFTSM) control theory. To validate the performance and advantages of the proposed algorithm, simulations are implemented. According to the simulation results, the proposed algorithm has better integrated performance in reducing the collision risk and improving the driving comfort and fuel economy of the smart car compared with the existing single AEBS.

Emergency braking is affected by the use of cruise control

ABSTRACTObjective: We compared the differences in the braking response to vehicle collision between an active human emergency braking (control condition) and cruise control (CC) or adaptive cruise control (ACC).Methods: In 11 male subjects, age 22 to 67 years, we measured the active emergency braking response during manual driving using the accelerator pedal (control condition) or in condition mimicking CC or ACC. In both conditions, we measured the brake reaction time (BRT), delay to produce the peak braking force (PBD), total emergency braking response (BRT + PBD), and peak braking force (PBF). Electromyograms of leg and thigh muscles were recorded during braking. The tonic vibratory response (TVR), Hoffman reflex (HR), and M-waves were recorded in leg muscles to explore the change in sensorimotor control.Results: No difference in PBF, TVR amplitude, HR latency, and Hmax/Mmax ratio were found between the control and CC/ACC conditions. On the other hand, BRT and PBD were significantly lengthened in the CC/ACC condition (240 ± 13 ms and 704 ± 70 ms, respectively) compared to control (183 ± 7 ms and 568 ± 36 ms, respectively). BRT increased with the age of participants and the driving experience shortened PBD and increased PBF.Conclusions: In male subjects, driving in a CC/ACC condition significantly delays the active emergency braking response to vehicle collision. This could result from higher amplitude of leg motion in the CC/ACC condition and/or by the age-related changes in motor control. Car and truck drivers must take account of the significant increase in the braking distance in a CC/ACC condition.