Braking Action Research Articles

On the Internal Bremsstrahlung accompanying β-decay and its potential relevance in the application of radioactive sources.

An in-depth analysis of the decay process for β-emitting radionuclides highlights, for some of them, the existence of high-order effects usually not taken into account in literature as considered negligible in terms of energy and yield, and referred to as Internal Bremsstrahlung (IB). This set of β-radionuclides presents, besides their β spectrum, a continuous γ emission due to the Coulomb field braking action on the emitted electron following the decaying nucleus. 
In this work, we review the theoretical and experimental studies on the IB process focusing on its actual importance for the pure β emitters. It emerges that there is no satisfactory model able to reproduce the experimental IB distribution for most of the investigated beta emitters and the several measurements are sometimes at odds with each other. Moreover, as recently demonstrated, the IB process can give a relevant contribution to the physics of beta emitters thus requiring its inclusion in the physics of the beta decay.
A discussion on the importance of considering the IB process in both applicative fields such as nuclear medicine, industrial applications, and research or calibration laboratories, and in other relevant fields of particle physics or astrophysics, such as the research on dark matter or neutrino mass, is presented.

Design, Simulation, Building, and Testing of a Microcontroller-Based Automatic Drowsiness Detection, Vehicle Braking, and Alert System

Aims: The premier practical aspect of this research drive is to propose, build, simulate, and evaluate an Arduino-built automatic vehicle driver drowsiness detection and alert system.
 Study Design: The pivotal role of a reliable braking system in vehicular safety cannot be overstated, particularly considering the escalating frequency of traffic accidents, notably prevalent in Indonesia where human factors take center stage as the primary accident catalyst. An insightful poll underscores physical fatigue or drowsiness while driving as the foremost concern. Acknowledging the nuanced disparities between conventional air systems and electric systems, this research strategically directs its attention toward crafting a new system characteristic that mirrors the efficiency of the existing systems.
 Place and Period of Study: The research action engaged by the authors in a bunch of two students under the command of a professor as a part of one of his course capstone projects for the Bachelor of Science in Electrical and Electronic Engineering degree at the American International University Bangladesh (AIUB), Dhaka, Bangladesh. The authors performed their investigative tasks at AIUB from June 2023 to January 2024.
 Methodology: Recognizing the imperative to fortify vehicular safety, the integration of artificial intelligence emerges as a vital solution to assist drivers in ensuring the safety of individuals both within and outside the vehicle. This focused study, tailored specifically for Electric Vehicles (EVs), centers on the innovative application of object and distance identification methodologies to provide a comprehensive braking action indicator. Leveraging a minicomputer and a sophisticated neural network approach, images captured by a stereo camera undergo meticulous machine learning processes. This facilitates the precise categorization and measurement of distances between objects, with subsequent priority decisions determining the optimal degree of braking action. Employing an intelligent methodology, specifically fuzzy logic, the study demonstrates a successful outcome by constructing a curve while concurrently enhancing the dynamics of the pre-existing system. Moreover, a finely tuned Pulse Width Modulation (PWM) signal for braking, lasting 10 milliseconds, is intricately devised based on the study's discerning results.
 Results: The system is simulated in Proteus and tested in real time to check its functionality. The outcomes of the test showed that the system can produce the appropriate signals based on the detection of any kind of driver’s drowsiness and can stop the car.
 Conclusion: This comprehensive approach ensures the seamless replacement of components while concurrently elevating the overall performance of the braking system

Active and Reactive Power Management of Grid Connected Variable Speed Permanent Magnet Generator Wind Turbine.

In this paper a variable speed generating unit is considered, equipped with permanent magnet generator. The system analysis involves appropriate models for the generator, the static converter and the grid. Moreover, an asynchronous generator mounted on the same rotor with dump loads ensures braking actions and management of the active power produced. The modelling of the generator and power electronics interface is presented and checked with measurements realized in a 2 kW prototype in dynamic conditions.

Comparative Analysis of Controller Effect on Anti-lock Braking System Performance using MATLAB/ Simulink

Active safety features aim to avoid or reduce road accidents, in contrast to passive safety features, which are meant to protect occupants after a collision. An Anti-lock Braking System (ABS) is a category of active safety breaking that stops the wheels from locking during a braking action, keeping them in contact with the ground. Different researchers have proposed various control methods for developing the ABS controller. A controller is effective if it reduces stopping distance and time and maintains the desired slip ratio. In this research paper, a quarter-vehicle model is utilized to evaluate the effect of the controller on the Anti-lock Braking System (ABS) and study longitudinal braking dynamics. The Bang-bang controller, Proportional Integral Derivative (PID) controller and the Fuzzy Logic (FL) controller for the ABS were modelled in MATLAB/ Simulink software. A feedback controller that abruptly flips between the two states is called a bang-bang controller, also called an on-off, two-step, or hysteresis controller. PID (Proportional - Integral - Derivative) parameters were obtained using the Ziegler Nichols tuning Method. A comparative analysis of the controllers concerning braking distance, braking time, and normalized relative slip was performed. ABS with the Fuzzy Logic (FL) controller outperforms both the Proportional - Integral - Derivative (PID) and bang-bang controllers.

Fully Convolutional Neural Network for Vehicle Speed and Emergency-Brake Prediction.

Ego-vehicle state prediction represents a complex and challenging problem for self-driving and autonomous vehicles. Sensorial information and on-board cameras are used in perception-based solutions in order to understand the state of the vehicle and the surrounding traffic conditions. Monocular camera-based methods are becoming increasingly popular for driver assistance, with precise predictions of vehicle speed and emergency braking being important for road safety enhancement, especially in the prevention of speed-related accidents. In this research paper, we introduce the implementation of a convolutional neural network (CNN) model tailored for the prediction of vehicle velocity, braking events, and emergency braking, employing sequential image sequences and velocity data as inputs. The CNN model is trained on a dataset featuring sequences of 20 consecutive images and corresponding velocity values, all obtained from a moving vehicle navigating through road-traffic scenarios. The model's primary objective is to predict the current vehicle speed, braking actions, and the occurrence of an emergency-brake situation using the information encoded in the preceding 20 frames. We subject our proposed model to an evaluation on a dataset using regression and classification metrics, and comparative analysis with existing published work based on recurrent neural networks (RNNs). Through our efforts to improve the prediction accuracy for velocity, braking behavior, and emergency-brake events, we make a substantial contribution to improving road safety and offer valuable insights for the development of perception-based techniques in the field of autonomous vehicles.

Neural Adjustments during Repeated Braking and Throttle Actions on a Motorcycle Setup.

The aim of the study was to assess neuromuscular changes during an intermittent fatiguing task designed to replicate fundamental actions and ergonomics of road race motorcycling. Twenty-eight participants repeated a sequence of submaximal brake-pulling and gas throttle actions, interspaced by one maximal brake-pulling, until failure. During the submaximal brake-pulling actions performed at 30% MVC, force fluctuations, surface EMG, maximal M-wave (Mmax) and H-reflex were measured in the flexor digitorum superficialis. At the end of the task, the MVC force and associated EMG activity decreased (P<0.001) by 46% and 26%, respectively. During the task, force fluctuation and EMG activity increased gradually (106% and 61%, respectively) with respect to the pre-fatigue state (P≤0.029). The Mmax first phase did not change (P≥0.524), whereas the H-reflex amplitude, normalized to Mmax, increased (149%; P≤0.039). Noteworthy, the relative increase in H-reflex amplitude was correlated with the increase in EMG activity during the task (r=0.63; P<0.001). During the 10-min recovery, MVC force and EMG activity remained depressed (P≤0.05) whereas H-reflex amplitude and force fluctuation returned to pre-fatigue values. In conclusion, contrarily to other studies, our results bring forward that when mimicking motorcycling brake-pulling and gas throttle actions, supraspinal neural mechanisms primarily limit the duration of the performance.

Enhancing Autonomous Vehicle Stability through Pre-Emptive Braking Control for Emergency Collision Avoidance

A pre-emptive braking control method is proposed to improve the stability of autonomous vehicles during emergency collision avoidance, aiming to imitate the realistic human driving experience. A linear model predictive control is used to derive the front wheel steering angle to track a predefined fifth-degree polynomial trajectory. Based on a two-degrees-of-freedom (DOF) vehicle dynamics model, the maximum stable vehicle speed during collision avoidance can be determined. If the actual vehicle speed exceeds the maximum stable vehicle speed, braking action will be applied to the vehicle. Furthermore, four-wheel steering (4WS) control and direct yaw moment control (DYC) are employed to further improve the stability of the vehicle during collision avoidance. Simulation results under a double lane change scenario demonstrate that the control system incorporating pre-emptive braking, 4WS, and DYC can enhance the vehicle stability effectively during collision avoidance. Compared to the 2WS system without pre-emptive braking control, the maximum stable vehicle speed of the integrated control system can be increased by at least 56.9%. The proposed integrated control strategy has a positive impact on the safety of autonomous vehicles, and it can also provide reference for the research and development of autonomous driving systems.

Experimental Validation of a Driver Monitoring System

This paper presents an analysis of the risk of neck injury in vehicle occupants as a consequence of an impact. A review of the formulation of indexes that are used in the assessment and investigation of neck injury risk is discussed with the aim of providing a new, more appropriate index using suitable sensorized equipment. An experimental analysis is proposed with a new driver monitoring device using low-cost sensors. The system consists of wearable units for the head, neck, and torso where inertial measurement sensors (IMU) are installed to record data concerning the occupant’s head, neck, and torso accelerations while the vehicle moves. Two laser infrared distance sensors are also installed on the vehicle’s steering wheel to record the position data of the head and neck, as well as an additional IMU for vehicle acceleration values. To validate both the device and the new index, experiments are designed in which different sensorized volunteers reproduce an emergency braking maneuver with an instrumented vehicle at speeds of 10, 20, and 30 km/h before the beginning of any braking action. The neck is particularly sensitive to sudden changes in acceleration, so a sudden braking maneuver is enough to constitute a potential risk of cervical spine injury. During the experiments, large accelerations and displacements were recorded as the test speed increased. The largest accelerations were obtained in the experimental test at a speed of 30 km/h with values of 19.17, 9.57, 9.28, and 5.09 m/s2 in the head, torso, neck, and vehicle, respectively. In the same experiment, the largest displacement of the head was 0.33 m and that of the neck was 0.27 m. Experimental results have verified that the designed device can be effectively used to characterize the biomechanical response of the neck in car impacts. The new index is also able to quantify a neck injury risk by taking into account the dynamics of a vehicle and the kinematics of the occupant’s head, neck, and torso. The numerical value of the new index is inversely proportional to the acceleration experienced by the vehicle occupant, so that small values indicate risky conditions.

Lichens as bio-monitors of polycyclic aromatic hydrocarbons: Measuring the impact of features and traffic patterns

The role of road characteristics, including gradient and speed control devices, in influencing emission dynamics remains to be fully elucidated. Most studies have focused on fuel consumption as an indirect indicator of sector emissions instead of directly quantifying specific pollutants, like polycyclic aromatic hydrocarbons (PAHs). This research approach is often due to the complexities involved in capturing these pollutants and their subsequent analysis. Bio-monitors, such as lichens, offer an economically viable method. Their wide distribution across various habitats enables the comparison of PAH levels in diverse environments. Against this background, The present work analyses the ability of tropical lichens to indicate the effect that traffic patterns and geometric design features of roads (traffic activity, road gradient, traffic control devices, and vehicular speed) have on the emission of PAH concentration. Results showed that PAHs in lichens strongly correlated with the road gradient (Spearman correlation, p<0.005 with R=0.98). Each 1% increase in road gradient implies a rise of 24 ngPAH/gLichen in National Road. Additionally, a trend coherent of PAH concentration with the vehicle speed profile was observed on Panamericana Road. Speed control devices were associated with higher concentrations of PAHs due to acceleration and braking actions that increment fuel consumption. Finally, the results evidenced that lichens helped determine the source of aromatics and their carcinogenic potential using the diagnostic ratio of PAHs and the carcinogenic equivalence sum, respectively.

EEG-based emergency braking intention detection during simulated driving

BackgroundCurrent research related to electroencephalogram (EEG)-based driver’s emergency braking intention detection focuses on recognizing emergency braking from normal driving, with little attention to differentiating emergency braking from normal braking. Moreover, the classification algorithms used are mainly traditional machine learning methods, and the inputs to the algorithms are manually extracted features.MethodsTo this end, a novel EEG-based driver’s emergency braking intention detection strategy is proposed in this paper. The experiment was conducted on a simulated driving platform with three different scenarios: normal driving, normal braking and emergency braking. We compared and analyzed the EEG feature maps of the two braking modes, and explored the use of traditional methods, Riemannian geometry-based methods, and deep learning-based methods to predict the emergency braking intention, all using the raw EEG signals rather than manually extracted features as input.ResultsWe recruited 10 subjects for the experiment and used the area under the receiver operating characteristic curve (AUC) and F1 score as evaluation metrics. The results showed that both the Riemannian geometry-based method and the deep learning-based method outperform the traditional method. At 200 ms before the start of real braking, the AUC and F1 score of the deep learning-based EEGNet algorithm were 0.94 and 0.65 for emergency braking vs. normal driving, and 0.91 and 0.85 for emergency braking vs. normal braking, respectively. The EEG feature maps also showed a significant difference between emergency braking and normal braking. Overall, based on EEG signals, it was feasible to detect emergency braking from normal driving and normal braking.ConclusionsThe study provides a user-centered framework for human–vehicle co-driving. If the driver's intention to brake in an emergency can be accurately identified, the vehicle's automatic braking system can be activated hundreds of milliseconds earlier than the driver's real braking action, potentially avoiding some serious collisions.

Predicting the Severity of Runway Excursions from Aviation Safety Reports

Despite all technological advances, runway excursions (REs) remain among the most frequent aviation accidents. Several factors have been shown to contribute to RE incidents. Although different studies have addressed the prediction of REs using quantitative data, the present study contributes to RE research by applying advanced quantitative analysis methods using text mining of voluntarily submitted reports to predict aircraft damage as the outcome of a runway excursion event. The study also intended to unveil relevant predictors from the voluntary safety reports data. The random forest model presented better results than the naïve Bayes and the gradient boosting approaches. The positive predictive value of the random forest method was measured at 74.90%, which is in contrast to 63.58% of the naïve Bayes method and 72.56% of the gradient boosting method. Although several generalizable topics were identified in the reports (such as runway and touchdown zone characteristics, flight control during approach, aircraft malfunction, weather factors, and runway surface and braking action conditions), no defined predictors for runway excursion events resulting in aircraft damage could be established.

Particle Debris Generated from Passenger Tires Induces Morphological and Gene Expression Alterations in the Macrophages Cell Line RAW 264.7

Air pollution in the urban environment is a topical subject. Aero-suspended particles can cause respiratory diseases in humans, ranging from inflammation to asthma and cancer. One of the components that is most prevalent in particulate matter (PM) in urban areas is the set of tire microparticles (1-20 μm) and nanoparticles (<1 μm) that are formed due to the friction of wheels with asphalt and are increased in slow-moving areas that involve a lot of braking actions. In this work, we studied the effect that microparticles generated from passenger tires (PTWP, passenger tire wear particles) have in vitro on murine macrophages cells RAW 264.7 at two concentrations of 25 and 100 μg/mL, for 24 and 48 h. In addition to the chemical characterization of the material and morphological characterization of the treated cells by transmission electron microscopy, gene expression analysis with RT-PCR and active protein analysis with Western blotting were performed. Growth curves were obtained, and the genotoxic effect was evaluated with a comet assay. The results indicate that initially, an induction of the apoptotic process is observable, but this is subsequently reversed by Bcl2. No genotoxic damage is present, but mild cellular abnormalities were observed in the treated cells.

A Flywheel-Based Regenerative Braking System for Railway Vehicles

Abstract Regenerative braking is a technique that employs electric motors to convert the dynamic mechanical energy from the motor’s spinning rotor and any attached loads into electricity. However, such a type of regenerative braking can only slow but not stop the vehicle because there is too little energy to excite the motor acting as a generator at low speeds. Therefore, this paper presents a unique flywheel-based regenerative braking system for railway vehicles. This system is supposed to meet high safety and comfort expectations in all operating conditions. The braking action control of this system should allow braking of empty or loaded vehicles according to load, the anti-blockage braking action of wheels and prevent wheel-slide during braking or wheel slip during acceleration. The new regenerative braking system under development, like any kinetic energy recovery system, requires the application of continuously variable transmission. The essence of the new solution is to design and build this type of variable transmission using only one planetary gear controlled through the powertrain control module for an electric motor cooperating concurrently. This paper describes complete modelling and simulation realisation on a closed-loop servomotor drive, which cooperates with the variable transmission of the regenerative braking system based on the Scilab/Xcos environment.

Efficient Regenerative Braking Strategy Aimed at Preserving Vehicle Stability by Preventing Wheel Locking

This paper presents a regenerative braking logic to be adopted on full electric vehicles with front, rear-drive or all-wheel drive with one motor for each axle, which aims at maximizing energy recovery under braking, avoiding wheel locking thus preventing vehicle instability. The logic implies the adoption of a brake-by-wire system i.e., the hydraulic braking system can be activated independently from the brake pedal. As a matter of fact, with the pedal pressed, the logic gives priority to the braking action of the electric motor(s) which acts as a generator, thus maximizing energy recovery, however taking into account various limitations, including the wheel locking limit, ensuring the stability of the vehicle. When the electric motor cannot satisfy the regenerative torque request, braking is integrated with the help of the hydraulic brakes, whose contribution aims to bring the braking towards a condition of optimal braking distribution. The front and rear hydraulic systems must therefore be independent of each other and controllable separately. This logic was tested via simulation, and it emerged that, on the WLTC driving cycle, the logic saved about 30% in consumption compared to the same vehicle without regenerative recovery, and about 23% compared to a logic commonly adopted on the market. On cycle US06, it saves about 24% and 19%, respectively.

Detailed CFD transient heat transfer modelling in a brake friction system

In a brake system the contact between the friction grooves of the discs and the steel elements leads to a consistent heat dissipation that is excised by the oil thermal vector in its flowing along the grooves channels. These are very complicated physical system were many variables may be taken in consideration, first of all the extensive integration of the different time scheduling actions that take place and that characterizes the translation of the interacting bodies and the correspondent deceleration, and also the heat dissipation derived from this contact. The simulations of similar physical system may result in too expensive computational efforts leading to very complicated simulations that need to take in account an extensive coupling between many physics, first of all the solid/liquid heat transfer and then the turbulence and the bi-phase models for the oil/air mixture that serves as thermal refrigerator vector in the braking system. Herein a simplified 3D model that focuses only on a discs / piston coupling is presented were the braking action is simulated in a simplified way modelling the velocity of rotation of the elements and the dissipated heat during the braking cycle. Focusing on the grooves channels the oil flows and thermal averaged flows are presented and detail contours are used to characterise a single brake event.

Does Motorcycle Autonomous Emergency Braking (MAEB) mitigate rider injuries and fatalities? Design of effective working parameters and field test validation of their acceptability

Autonomous Emergency Braking on Motorcycles (MAEB) was shown to be a promising technology to improve Powered-Two-Wheeler (PTW) user safety, deploying autonomously a braking action to reduce impact speed when pre-crash conditions are detected. The applicability of MAEB with effective working parameters still needs to be proved in real-world working conditions. The goal of this paper is to define MAEB working parameters for effective mitigation of injuries and to validate their real-world applicability through field tests in representative riding scenarios of intervention.The results of previous studies and injury risk functions for motorcyclists were employed to define a set of MAEB working parameters (duration of intervention ≥ 0.60 s, target declaration of 0.5 g, fade-in jerk of 2 g/s) which enable MAEB impact speed reductions capable of mitigating injuries. Field tests were then carried out involving common riders as participants and employing a test protocol consistent with previous studies. Automatic Braking (AB) interventions were intended to reproduce unexpected MAEB activations in different riding conditions including straight-line riding and lane-change manoeuvres reproducing an avoidance action. The tests were carried out using a sport-touring motorcycle provided with extension arms to prevent capsizing and equipped with an automatic braking system which was activated remotely by researchers.Thirty-one participants involved in the test experienced AB interventions riding in straight-line and lane-change at a mean speed ranging from 41 km/h to 47 km/h. In the 0.5 g test session, the AB system performed mean deceleration 0.48 g reached with 2.0 g/s fade-in jerk for a duration of around 1.1 s. Both subjective assessment and vehicle data analysis indicated that the participants were consistently able to easily control the vehicle during the automatic braking interventions and were always able to complete a lane-change manoeuvre. No signal of incipient loss of vehicle control was recorded in on-board videos nor identified in the behaviour and body movement by participants. The tested MAEB working parameters performed speed reductions capable to reduce impact speed up to 15 km/h, potentially reducing serious injuries (MAIS3+) by 15 %.The proposed MAEB working parameters were proved as effective in reducing vehicle speed and feasible from end-users’ perspective. The designed MAEB intervention was shown to be applicable in straight line riding and to allow the rider to manoeuvre the vehicle for the execution of avoidance actions.

When is it too late to brake?

This paper provides a simulation analysis of the braking action that would prevent untripped rollovers of long combination vehicles (LCV) in turns when the entry speed into a turn exceeds the vehicle’s threshold. A co-simulation model is used to integrate the details of truck pneumatic brakes (developed in Simulink®) in a TruckSim® model. The models are validated with field-test data. Using the validated models, various braking initiation times (relative to the start of steering) are performed for a 150-ft J-turn. The simulation results reveal that at higher speeds, there is very little time for the driver to initiate braking before it is too late to avoid a rollover, referred to as Critical Brake Initiation Time (CBIT). For instance, at an entry speed of 40 mph (64 km/hr), applying the brakes for a fully-loaded truck beyond 1.0s would not prevent a rollover. The results also indicate that increasing the speed by 25% from 40 mph (64 km/hr) to 50 mph (80 km/hr), reduces CBIT by 90%, from 1.0s to 0.1s. The effect of cargo load on CBIT is less dramatic than speed. At 40 mph (64 km/hr), increasing the cargo load by 47%, from 15,000 lb. (6800 kg) to 22,000lb. (10,000 kg), decreases the CBIT by 17%, from 1.2s to 1.0s.

Energy-Saving Metro Train Timetable Optimization Method Based on a Dynamic Passenger Flow Distribution

The operation of metro trains with a focus on energy savings can effectively reduce operating costs and carbon emissions. Reducing traction energy consumption and increasing the utilization efficiency of regenerative braking energy are two important energy-saving approaches that are closely related to the metro train interstation running strategy and timetable. Changes in train mass caused by dynamic changes in passenger flow represent one of the important factors affecting the energy consumption and energy-saving operation of metro trains. In this study, the differences in the temporal and spatial distributions of metro line passenger flow were specifically considered, and an energy-saving metro train timetable optimization method focused on the dissipative regenerative braking energy utilization mode was studied. First, a logistic function is used to fit the passenger flow pattern of the origin-destination (OD) station pairs, and the number of passengers getting on and off at each station is derived by establishing the OD dynamic demand matrix for the entire metro line. Then, the passenger load in each station segment is calculated. Next, a timetable optimization model is established to minimize the net energy consumption based on the load difference between station segments and the train motion equation. The interstation running time and dwell time of the metro train are optimized to increase the amount of regenerative braking energy used during the overlap time between the traction and braking actions of adjacent trains in the train operation timetable. A particle swarm optimization and genetic algorithm (PSO-GA) structure is designed to solve the model. The PSO-GA structure has PSO as the main body and integrates the chromosome crossover and mutation operations of the GA into the iterative process to improve the search efficiency of the algorithm. Finally, the proposed method and model are tested based on the actual data of a metro line in Qingdao, China. The goodness of fit of the passenger flow pattern is 0.997. The energy consumption during the study period is reduced by 5169.67 kW h using the optimized timetable. The energy-saving efficiency decreases by 12.18% at a constant OD ratio during the entire travel time and by 20.23% at the same constant load for all station segments. The results of the case analysis prove the effectiveness of the proposed method and model. In addition, the energy-saving timetable can be better optimized by considering the differences in temporal and spatial distributions of dynamic passenger flow.

Gain-Scheduled Steering and Braking Coordinated Control in Path Tracking of Intelligent Heavy Vehicles

Abstract In the context of intelligent transportation system and advanced vehicle control system, higher requirements for intelligent control and coordinated control of heavy vehicles (HVs) have been proposed. Automatic path tracking control is essential for automatic driving of heavy vehicles. However, uncertain lateral disturbances and time-varying characteristics of system parameters make it difficult to guarantee roll stability and path tracking accuracy during automatic path tracking. This paper investigates the coordination of active front steering (AFS) and direct yaw moment control (DYC) in the automatic path tracking system for intelligent heavy vehicles. The main idea is to adjust the braking action according to the rollover risk evaluated by a specific rollover index (RI). The coordination of steering and braking systems and the robustness of the system against time-varying parameters are achieved by a gain-scheduled linear parameter varying (LPV) controller. Based on the linear matrix inequality (LMI) framework, the LPV controller is synthesized to ensure the robust H∞ performance against external disturbances. Simulation results show that the proposed gain-scheduled LPV/H∞ control strategy can enhance roll stability and path tracking accuracy in the path tracking process of intelligent heavy vehicles.

Investigating the Cognitive Response of Brake Lights in Initiating Braking Action Using EEG

Half of all road accidents result from either lack of driver attention or from maintaining insufficient separation between vehicles. Collision from the rear, in particular, has been identified as the most common class of accident in the UK, and its influencing factors have been widely studied for many years. Rear-mounted stop lamps, illuminated when braking, are the primary mechanism to alert following drivers to the need to reduce speed or brake. This paper develops a novel brain response approach to measuring subject reaction to different brake light designs. A variety of off-the-shelf brake light assemblies are tested in a physical simulated driving environment to assess the cognitive reaction times of 22 subjects. Eight pairs of LED-based and two pairs of incandescent bulb-based brake light assemblies are used and electroencephalogram (EEG) data recorded. Channel Pz is utilised to extract the P3 component evoked during the decision making process that occurs in the brain when a participant decides to lift their foot from the accelerator and depress the brake. EEG analysis shows that both incandescent bulb-based lights are statistically slower to evoke cognitive responses than all tested LED-based lights. Between the LED designs, differences are evident, but not statistically significant, attributed to the significant amount of movement artifact in the EEG signal.