Automatic Braking Research Articles

ДО ПИТАННЯ ВПРОВАДЖЕННЯ В УКРАЇНІ ЄВРОПЕЙСЬКИХ ПІДХОДІВ ДО ВИПРОБУВАННЯ ПНЕВМАТИЧНИХ ГАЛЬМІВНИХ СИСТЕМ СІЛЬСЬКОГОСПОДАРСЬКИХ ПРИЧЕПІВ

Summary The purpose of the research: implementation in Ukraine of the technical requirements of the Delegated Regulation of the EU Commission 2015/68 on the parameters of pneumatic braking systems of agricultural trailers by preparing a methodological and technical base. Research methods: research was conducted in accordance with the methods specified in Commission delegated Regulation (EU) 2015/68, 2015; SOU L. Pohorilyi UkrNDIPVT 01.1-37-04604309-008 and DSTU 1.5. The main indicators of the effectiveness of the pneumatic braking system of the trailer, which must be determined during the conformity assessment tests, are: the effectiveness of the braking system during cold brakes (type 0 test); loss of braking system efficiency with heated brakes (type 1 test); effectiveness of emergency automatic braking; brake activation time; capacity of energy storage devices. Research results: To conduct testing of the pneumatic brake system of the trailer at L. Pohorilyi UkrNDIPVT developed, manufactured and adjusted a pneumatic braking simulator that would replace a tractor to actuate the braking system of a trailer, and serial measuring equipment was used. As a result of the work carried out, the operating manual «Brake-1. Efficiency of the braking system»; developed, agreed and approved standard of L. Pohorilyi UkrNDIPVT «Agricultural machinery. Pneumatic braking systems of agricultural trailers. Test methods». The research results were implemented during tests for the European type approval of agricultural trailers produced by VARZ LLC. Conclusions. The developed methodical and technical bases contribute to the implementation in Ukraine of the technical requirements of the Delegated Regulation of the EU Commission 2015/68 on the parameters of pneumatic braking systems of agricultural trailers and make it possible to carry out testing of the equipment of domestic manufacturers without taking them outside Ukraine and to receive certificates of approval of the type of the European sample. Keywords: agricultural trailers, pneumatic braking system; testing; braking simulator; type approval certificate.

A Review Paper on Development of e-Vehicles

Abstract: In this paper, we present a detailed chronological analysis of the achievements in autonomous automation, which is an area of interest for researchers. This paper can be useful for understanding the trends in autonomous vehicletechnology in the past, present, and future. The technology of autonomous vehicles has drastically changed since the 1920s, when radio controlled vehicles first appeared. Electric cars powered by embedded circuits on the road became fairly autonomous in the next few decades. By the 1960s, autonomous cars had similar electronic guide systems. The 1980s saw the introduction of vision guided autonomous vehicles, a major milestone in technology that we continue to use today. In modern cars, semi-autonomous features such as lane keeping,automatic braking, and adaptive cruise control are based on these systems. An extensive network of visionguided systems is the future of autonomous vehicles. It is predicted that the majority of companieswill launch fully autonomous vehicles by the turn of the century. We are entering an era of safe, comfortable, and efficient transportation with autonomous vehicles.

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.

IS THE MALAYSIAN DRIVER READY TO USE AN AUTOMATIC EMERGENCY BRAKING (ABS) SYSTEM?

Autonomous Emergency Brake (AEB) is the safety system technology for the vehicle that functions to prevent a crash or reduce the high impact of injury or accident. AEB systems are designed to assist the driver only in emergencies situation and to ensure that the driver is always in control of the vehicle. This study aims to identify influencing factors of drivers that continuance intention to use an Automatic Emergency Braking (AEB) system in Malaysia. This study is supported by Technology Acceptance Model (TAM). This study shows that 3 factors have significantly related to continuance intention; perceived ease of use, attitude, and perceived usefulness. In contrast, the safety of drivers was found to be insignificantly related to the continuance intention to use automatic emergency braking among drivers in Malaysia. The AEB system is a sophisticated system that helps alert the driver to a lack of human response. Therefore, the successful use of this system needs the full support of the government and industries.

Monocular thermal imaging for pedestrian detection and ranging

To react to the presence of pedestrians, an automated braking system must first find the pedestrian(s) including locations relative to the automobile both in angular position and in distance. In the daytime, cameras and radar can provide the necessary information, but this combination, which requires ambient or active illumination, fails at night. Passive thermal sensors are now being enlisted to dramatically improve imaging at night, whereas substantial effort is underway to assure proper fusing of object information from the thermal sensor with other sensors on the automobile. To simplify the acquisition of information needed to make valid automated braking decisions at night, a camera system with a thermal image sensor was developed that identifies pedestrians and labels each identified pedestrian with location and distance data. The camera utilizes a single uncooled custom microbolometer sensor and a software suite implementing artificial intelligence and machine learning capabilities, running on a combination of convolutional neural networks and fusion processing to provide the data that an automobile host computer needs to implement fast, safe, and accurate automatic braking. We present details of the system construction and operation as well as initial test results showing the potential this technology has to dramatically reduce pedestrian fatalities at night as well as augment safety across all conditions, whether day, night, fog, rain, snow, dust, sun glare, or headlight glare.

Predicting the impact on road safety of an intersection AEB at urban intersections. Using a novel virtual test field for the assessment of conflict prevention between cyclists/pedelecs and cars

With the rise of Advanced Driver Assistant Systems (ADAS) and the introduction of Highly Automated Driving (HAD), understanding and predicting road traffic accidents becomes increasingly important. Especially for the assessment of HAD/ADAS systems and of road safety, the precise prediction of the system’s impact on the occurrence of road traffic accidents is essential.Traffic simulations, as one option of virtual assessment, enable the assessment of safety systems in virtual test fields. By modelling the human driver, it is possible to simulate and predict future accident constellations and accident severities. In addition, the influence of vehicle factors, such as the presence of safety systems or their characteristics, on the occurrence of accidents can be investigated.This article elucidate virtual assessment by executing the whole procedure for an automatic emergency braking (AEB) system capable to detect cyclists at urban intersections. The authors implement a new driver behaviour model (DReaM) to predict road traffic accidents at urban intersections. Using the AEB as an example, the influence of different sensor opening angles on crash frequency is investigated by varying the sensors in three steps: 100°, 180°, and 210°. A total of 240,000 situations are simulated of which 6,674 are crashes. Within the two investigation scenarios, the crash frequency is reduced by up to 88.43%, or 93.92% by introducing the AEB system.The article shows that virtual assessment enables the prediction of new safety systems regarding road safety at early design stages. In addition, different system characteristics can be compared efficiently, e.g., the sensor opening angle.

Automatic Deceleration and Braking Research for Driverless Vehicles in Rail Transit based on Internet of Vehicles

With the development of vehicle networking, driverless technology has also seen great progress. In this paper, we mainly study the automatic deceleration and braking method of driverless vehicles in rail transportation. Firstly, Radio Frequency Identification (RFID) technology was briefly introduced. Then, based on RFID, a graded deceleration and braking scheme was designed to position driverless trains through RFID and control the deceleration of vehicles when they reach different subintervals, so that the speed of the vehicles was 0 when they reached the stopping point. The experimental results showed that the reading distance of the system met the requirements of practical use even when the transmitting frequency was small. In the simulation experiment, the system operated stably and the errors of actual speed and the target speed between different sub-intervals were small, which ensured that the vehicle stopped precisely at the stopping position. The experimental results verify that the proposed method is effective. The method can be further promoted and applied in practice.

Part A. Decision-making and path planning based on skilled driver’s performance for emergency obstacle avoidance

Emergency Lane Change (ELC) is a strategic maneuver for collision avoidance (CA) at high speeds where there is an accident risk. In this paper, two algorithms are designed for CA at high speeds based on the vehicle’s initial speed and the distance of the obstacle. The first algorithm selects the more suitable case between Automatic Emergency Braking (AEB) and ELC systems to prevent an accident. Suppose there was no choice except a rapid Lane Change (LC); the second algorithm does path planning for an ELC. The most critical issue in the ELC is time because this maneuver’s duration is less than 2 or 3 s on the dry or wet road. So, designing a fast and safe path planning algorithm is very important. To solve this problem, with a seven-degrees of freedom vehicle dynamic’s equations and the skilled driver behavior manner in the ELC maneuver, scrollable and stable paths for initial speeds of 75–125 km/h are extracted. Then, a neural network (NN) is trained based on this simulated data. The main feature of this network is to design stable paths as fast as possible. Also, this network can plan the paths in the various road friction coefficients between 0.5 and 1. The trajectory is selected based on two geometric constraints including, including (1) not colliding with obstacles and (2) not crossing the road boundary.

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.

SDC-Net: End-to-End Multitask Self-Driving Car Camera Cocoon IoT-Based System

Currently, deep learning and IoT collaboration is heavily invading automotive applications especially in autonomous driving throughout successful assistance functionalities. Crash avoidance, path planning, and automatic emergency braking are essential functionalities for autonomous driving. Trigger-action-based IoT platforms are widely used due to its simplicity and ability of doing receptive tasks accurately. In this work, we propose SDC-Net system: an end-to-end deep learning IoT hybrid system in which a multitask neural network is trained based on different input representations from a camera-cocoon setup installed in CARLA simulator. We build our benchmark dataset covering different scenarios and corner cases that the vehicle may expose in order to navigate safely and robustly while testing. The proposed system aims to output relevant control actions for crash avoidance, path planning and automatic emergency braking. Multitask learning with a bird's eye view input representation outperforms the nearest representation in precision, recall, f1-score, accuracy, and average MSE by more than 11.62%, 9.43%, 10.53%, 6%, and 25.84%, respectively.

Simulation Analysis of Automatic Emergency Braking System under Constant Steer Conditions

Simulation Analysis of Automatic Emergency Braking System under Constant Steer Conditions

Calibrating and Comparing Autonomous Braking Systems in Motorized-to-Non-Motorized-Vehicle Conflict Scenarios

Assuring the safety of all road users, including non-motorized vehicles, is important in the autonomous driving environment. Autonomous emergency braking (AEB) systems have provided an effective way for automated vehicles to avoid collisions with the less easily detectable non-motorized vehicles. Automatic preventive braking (APB) is a new method proposed by Mobileye that promises to reduce crashes without reducing traffic throughput, but APB’s effectiveness has not yet been evaluated. This study therefore calibrates and compares the performance of APB with that of one-stage and three-stage AEB braking systems in safety-critical events (SCEs) between motorized and non-motorized vehicles, using SCEs extracted from the Shanghai Naturalistic Driving Study and simulated in MATLAB’s Simulink. The evaluation results, which consider both safety and conservativeness, show that 1) one-stage AEB with a deceleration of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.5\,\text {m/s}^{2}$ </tex-math></inline-formula> and a time-to-collision threshold of 1.6 seconds can prevent all SCEs from becoming crashes; 2) APB has the best driving stability but its safety performance is inferior to that of the two AEB systems; 3) APB’s deceleration process is easily affected by its pre-defined parameters and changing kinetic parameters, which may be one cause of its crashes; 4) AEB’s time-triggered braking process is more consistent and reliable than APB’s distance-triggered process.

Study on the Control Algorithm of Automatic Emergency Braking System (AEBS) for Commercial Vehicle Based on Identification of Driving Condition

Automatic emergency braking systems (AEBS) significantly improve the active safety performance of commercial vehicles, but their effectiveness is affected by the vehicle’s driving conditions, which mainly include the vehicle load and road conditions. In order to improve the adaptability of the AEBS, an AEBS control strategy with adaptive driving conditions was proposed and validated using a simulation and experimentation. This AEBS control strategy was designed based on an estimation of the vehicle mass, the center of gravity position, road grade, and the tire-road friction coefficient. In the simulation and experimental verification, the braking deceleration and braking distance under different driving conditions were compared. The results show that the AEBS control strategy proposed in this paper can avoid collisions in all test scenarios and maintain a parking spacing of approximately 5 m. In an extreme test scenario with a full load and low tire–road friction, as compared with the fixed threshold control strategy, the warning can be issued 0.2 s earlier and the maximum intensity braking can be carried out 0.5 s earlier.

Developing an improved automatic preventive braking system based on safety-critical car-following events from naturalistic driving study data

In public road tests of autonomous vehicles in California, rear-end crashes have been the most common type of crash. Collision avoidance systems, such as autonomous emergency braking (AEB), have provided an effective way for autonomous vehicles to avoid collisions with the lead vehicle, but to avert false alarms, AEB tends to apply late and hard brake only if a collision becomes unavoidable. Automatic preventive braking (APB) is a new collision avoidance method used in Mobileye’s Responsibility-Sensitive Safety (RSS) model that aims to reduce crashes with a milder brake and decreased impact on traffic flow, but APB’s safety performance is inferior to that of AEB. This study therefore proposes three safety improvement strategies for APB, the addition of response time, safety buffer, and minimum following distance; and combines them in different ways into four improved APB systems, IP1-IP4. Simulating car-following safety–critical events (SCEs) extracted from the Shanghai Naturalistic Driving Study in MATLAB’s Simulink, the safety performance, conservativeness, and driving comfort of the four systems were evaluated and compared with the original APB system, two AEB systems, and human drivers. The results show that 1) IP4, the system that integrated all three strategies, outperformed the baseline APB and IP1-IP3 and prevented all SCEs from becoming crashes; 2) IP4 was slightly more conservative than AEB, but less conservative than RSS; 3) APB’s jerk-bounded braking profile improved driving comfort; and 4) higher deceleration was found in the two AEB systems (both 8.1 m/s2) than in IP4 (6.7 m/s2), but they failed to prevent all crashes. Our proposed APB system, IP4, can provide safe, efficient, and comfortable braking for AVs in car-following SCEs, and has the potential to be practically applied in vehicle collision avoidance systems.

Advanced Driver Assistance Systems in Top-Selling Vehicles in the United States: Cost, Vehicle Type, and Trim Level Disparities

How are driver assistance technologies available and priced across different vehicle models and trim levels? This study examines the availability of blind spot detection, automatic emergency braking, and forward collision warning on the top-selling passenger vehicles in the United States. We found that these technologies are not uniformly available and safety features are less likely to occur on base models or less-expensive trim packages, particularly of larger vehicles and among US versus international automakers. This represents a cause for concern due to the greater dangers posed by SUVs and light duty trucks and their popularity in the United States.

On the road safety benefits of advanced driver assistance systems in different driving contexts

Advanced Driver Assistance Systems (ADAS) have introduced several benefits in the vehicular industry, and their proliferation presents potential opportunities to decrease road accidents. The reasons are mainly attributed to the enhanced perception of the driving environment and reduced human errors. However, as environmental and infrastructural conditions influence the performance of ADAS, the estimation of accident reductions varies across geographical regions. This study presents an interdisciplinary methodology that integrates the literature on advanced driving technologies and road safety to quantify the expected impact of ADAS on accident reduction across combinations of road types, lighting, and weather conditions. The paper investigates the safety effectiveness of ADAS and the distribution of frequency and severity of road accidents across 18 driving contexts and eight accident types. Using road safety reports from the United Kingdom (UK), it is found that a high concentration of accidents (77%) occurs within a small subset of contextual conditions (4 out of 18) and that the most severe accidents happen in dark conditions on rural roads or motorways. The results of the safety effectiveness analysis show that a full deployment of the six most common ADAS would reduce the road accident frequency in the UK by 23.8%, representing an annual decrease of 18,925 accidents. The results also show that the most frequent accident contexts, urban-clear-daylight and rural-clear-daylight, can be reduced by 29%, avoiding 7,020 and 3,472 accidents, respectively. Automatic Emergency Braking (AEB) is the most impactful technology, reducing three out of the four most frequent accident categories – intersection (by 28%), rear-end (by 27.7%), and pedestrian accidents (by 28.4%). This study helps prioritise resources in ADAS research and development focusing on the most relevant contexts to reduce the frequency and severity of road accidents. Furthermore, the identified contextual accident hotspots can assist road safety stakeholders in risk mitigation programs.

SMIRK: A machine learning-based pedestrian automatic emergency braking system with a complete safety case

<h2>Abstract</h2> SMIRK is a pedestrian automatic emergency braking system that facilitates research on safety-critical systems embedding machine learning components. As a fully transparent driver-assistance system, SMIRK can support future research on trustworthy AI systems, e.g., verification & validation, requirements engineering, and testing. SMIRK is implemented for the simulator ESI Pro-SiVIC with core components including a radar sensor, a mono camera, a YOLOv5 model, and an anomaly detector. ISO/PAS 21448 SOTIF guided the development, and we present a complete safety case for a restricted ODD using the AMLAS methodology. Finally, all training data used to train the perception system is publicly available.

Implementation Resource Allocation for Collision-Avoidance Assistance Systems Considering Driver Capabilities

Various collision-avoidance assistance (CAA) systems, such as automatic emergency braking (AEB) and lane-keeping assistance (LKA), have been developed in the last decades to enhance the active safety of ground vehicles. Meanwhile, more electronic computing units (ECUs) have been embedded inside a vehicle to support the diversified CAA systems, which complicate the automotive electrical/electronic architecture and increase the cost. Instead of adding <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">extra</i> ECUs, we propose to allocate the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">existing</i> implementation resources, i.e., the available processor time and memory space to the CAA systems, per individual driver’s maneuver capabilities. As an illustrative example, we first show that two drivers can exhibit distinct maneuvers in a pre-crash situation on highway, according to which they can be classified as either steering-oriented or braking-oriented. Then, we design two CAA systems: an AEB and an LKA, based on the ultra-local model predictive control method. Furthermore, we show that by adjusting the prediction horizons of the two controllers, the implementation resources can be allocated to the two CAA systems in different fashions, which yields three control modes: standard mode, steering-enhanced mode, and braking-enhanced mode. Finally, by comparing the control performance of each driver-type/control-mode pair through both CarSim-Simulink joint simulations and driver-in-the-loop simulator experiments, we demonstrate that by allocating more resources to compensate for the weakness of a driver’s maneuver, the CAA systems can provide enhanced driving safety by consuming the same overall amount of the implementation resources.

Can the speed limit be used as a surrogate for the striking vehicle’s travel speed or delta-V in police-reported rear-end crashes?

Objective: A recent study by Kidd (2022) recommended that organizations evaluating front crash prevention (FCP) systems like automatic emergency braking and forward collision warning increase speed differentials in existing test scenarios from 25 mph to 45 mph to make the tests more representative of police-reported rear-end crashes. Kidd used the posted speed limit as a proxy for the striking vehicle’s travel speed prior to the crash. The current study used velocity data from event data recorders (EDRs) in rear-end crashes to evaluate this assumption. These same data were used to replicate another study (Farmer 2003) that showed the speed limit was a poor surrogate for delta-V in rear-end crashes. Method: A total of 11,199 crash records during 2017–2020 were extracted from the Crash Investigation Sampling System database. The analysis was restricted to 436 of these records that involved two vehicles with a front-to-rear manner of collision or rear-end crash configuration with EDR data from the striking vehicle. The relationships between the posted speed limit and striking-vehicle travel speed and between the speed limit and delta-V were modeled using regression. Results: On average, the speed limit overestimated striking-vehicle travel speed by 2 mph, but the relationship between the speed limit and travel speed was not linear. The speed limit reasonably approximated travel speed on roads with speed limits of 30 and 50 mph or higher. It slightly overestimated travel speed on roads with 40–45 mph speed limits and underestimated it on roads with limits of 25 mph or less. The probability that the striking vehicle’s travel speed was 25 mph or less on any road was 0.09. In contrast, the probability of the striking vehicle’s travel speed being 45 mph or less was 0.54 overall and 0.75 or higher for roads with a speed limit between 25 and 45 mph. As found in prior research, there was no significant relationship between the speed limit and delta-V. Conclusion: The posted speed limit was a reasonable surrogate for the striking vehicle’s travel speed prior to police-reported rear-end crashes on roads with a speed limit above 25 mph. It was not a reasonable surrogate for delta-V. Travel speeds on roads with speed limits of 25 mph or less were much higher than the speed limit, which suggests that existing scenarios used to evaluate FCP system performance represent travel speeds in fewer police-reported rear-end crashes than previously thought. Increasing speed differentials in existing FCP test scenarios to 45 mph would reflect the striking vehicle’s travel speed in three quarters of rear-end crashes on roads with a speed limit of 45 mph or less.

Automatic emergency braking/anti‐lock braking system coordinated control with road adhesion coefficient estimation for heavy vehicle

The road adhesion coefficient is a key factor influencing automatic emergency braking (AEB) and anti-lock braking system (ABS) safety control of trucks. With the fading factor introduced, and the covariance gain adjusted in real time, the strong tracking unscented Kalman filter (STUKF) algorithm is modified to estimate the road adhesion coefficient more accurately. Composed of an ABS fuzzy sliding mode controller (SMC) and an AEB controller, an AEB/ABS coordinated control strategy with an adhesion coefficient estimation is designed for a three-axle heavy vehicle. The control effects are verified through experiments on various road conditions based on a hardware-in-loop test platform. The test results show that the proposed control strategy has a better braking efficiency than the traditional AEB/ABS and AEB control strategy without adhesion coefficient estimation and can decrease braking distance by 8.4% and braking time by 5.9%, which is beneficial to the vehicle longitudinal safety.