Automatic Braking Research Articles

An ultrasonic sensor based automatic braking system to mitigate driving exhaustion during traffic congestion

Traffic congestion has been the most tiresome encounter since the initiation of vehicle advancement. Many braking systems have been designed by researchers in previous studies, but this study is primarily focused on a braking system that is affordable to all because it is based on a simple Arduino-controlled system. Moreover, it is assistive on everyday traffic commutes rather than highway driving, which is relatively rare for normal drivers. As more and more vehicles crowd the roads, the more problematic it is becoming for the drivers, which is ultimately leading toward increment in the frontal car accidents. In this study, an electro-mechanical braking system has been deployed that assists the driver during the jam-packs by measuring the exact distance between the driven and forthcoming vehicle or any obstacle by applying the brakes without the driver pressing the brake pedal to ultimately bring the vehicle to a halt without any fear of vehicle collision. An ultrasonic sensor is used at the front bumper grille that measures the distance between the two closing vehicles. The total time to halt the vehicle has been calculated as 0.6 s, while the critical distance for the sensor has been set as 1-m. Furthermore, the stepper motor drivers are set at the maximum current output of 2.5 amps with a 12-volt battery connected in parallel to the motors. It is found that theoretical stopping time is in good agreement with the experimental stopping time to completely press the brake pedal and halt the car.

Do Driver Characteristics and Crash Conditions Modify the Effectiveness of Automatic Emergency Braking?

<div class="section abstract"><div class="htmlview paragraph">Studies of automatic emergency braking (AEB) find that AEB-equipped vehicles are around half as likely to crash. This study examines whether driver characteristics and road and weather conditions modify this preventive effect of AEB.</div><div class="htmlview paragraph">Toyota production data were merged with police reported crash files from eight U.S. states for crash years 2015 up to 2019 by 17-digit vehicle identification number (VIN). Using a case-control design, this study investigated the relationship of AEB presence with being a case vehicle in a system-relevant crash (the striking vehicle in front-to-rear crash; n=30,056) versus an AEB non-relevant control vehicle (the struck vehicle in a front-to-rear crash; n=62,820). The analysis was stratified by driver characteristics and by weather and road conditions. Logistic regression modeled the relationship, controlling for exposure (vehicle-days) and possible confounding factors. The resulting odds ratios for AEB equipment from the separate models were compared to determine if the effect of AEB presence was modified by the characteristic or condition of interest.</div><div class="htmlview paragraph">Overall, AEB-equipped vehicles were 43% (p<0.001) less likely to be the striking (case) vehicle compared to non-equipped vehicles. However, the preventive effect of AEB was significantly lower among older drivers (over 65 years) compared to younger drivers; 29% less likely to be a striking vehicle (OR=0.71) versus 46% (OR=0.54), respectively. The effect of AEB was also lower in adverse weather conditions (rain, fog, snow) (OR=0.66) and on wet or snowy roads (OR=0.65), though these differences were not significant compared to clear weather and dry roads. The AEB effect was also lower among risk-taking drivers (alcohol-involved, speeding, or unrestrained) compared to non-risk-taking (OR=0.72 versus OR=0.59, respectively).</div><div class="htmlview paragraph">AEB prevents crashes, regardless of driver characteristics and environmental conditions. This study suggests, however, that the size of the effect is smaller among older and risk-taking drivers, and in adverse weather and road conditions.</div></div>

Effectiveness of Advanced Driver Assistance Systems in Preventing System-Relevant Crashes

<div class="section abstract"><div class="htmlview paragraph">This retrospective cohort study uses survival analysis to estimate the effectiveness of Toyota ADAS in helping prevent system-relevant crashes. Toyota production data were merged with police reported crash files from eight U.S. states for crash years 2015 up to 2019 by 17-digit vehicle identification number (VIN). System-relevant crash scenarios included: striking vehicle in front-to-rear, single vehicle run-off-the-road, same-direction sideswipe, head-on, and pedestrian struck. The study vehicle cohort included 11 Toyota/Lexus models, model years 2015 through 2018, sold in the eight study states. ADAS technologies studied included automatic emergency braking (AEB), lane departure warning (LDW), lane keeping assistance (LKA), blind spot monitoring (BSM) and pedestrian automatic emergency braking (PedAEB). Among the study cohort of 2,394,913 vehicles, police reported 308,490 crashes. The crude crash rate ratio (CRR) was 0.61 for AEB-equipped versus non-equipped vehicles. However, the CRR does not adjust for differences in ADAS-equipped versus non- equipped vehicles. To adjust for group differences (confounding factors), Cox proportional-hazards (CPH) regression modeled the relative risk (hazard ratio, HR) of being in a system-relevant crash for vehicles with versus without the ADAS. CPH modeling found that AEB-equipped vehicles were 43% less likely (HR=0.57) to be the striking vehicle in a front-to-rear crash compared to non-equipped vehicles. The analysis was also stratified to look at the effect in intersection versus non-intersection crashes. BSM-equipped vehicles were 4% less likely (HR=0.96) to be involved in a same-direction sideswipe, though the differences were not significant (p=0.252). LKA-equipped vehicles were 9% less likely (HR=0.91) to run off the road. LDW and LKA did not have a significant effect on risk of same-direction sideswipe or head-on crash. PedAEB-equipped vehicles were less likely to hit a pedestrian (HR=0.84), but the hazard ratios were marginally significant (p=0.114). This study contributes new evidence of the effectiveness of ADAS in helping prevent system-targeted crashes.</div></div>

Effectiveness of front crash prevention systems in reducing large truck real-world crash rates

Objective: Forward collision warning (FCW) and automatic emergency braking (AEB) have the potential to prevent or mitigate many large truck crashes. While these systems are known to be effective in passenger vehicles, less is known about their effectiveness in large trucks. The objective of this study was to estimate the effectiveness of these systems in reducing real-world crash rates of large trucks. Methods: Data on Class 8 trucks operating on limited-access highways during 2017–2019 were obtained from SmartDrive Systems. Detailed data on exposure measures and crash circumstances were extracted from video footage by both automated means and manual coding. Crash rates were compared by front crash prevention technology (FCW, AEB, neither), both for all police-reportable crashes overall and for relevant crash types. Results: FCW was associated with a statistically significant 22% reduction in the rate of police-reportable crashes per vehicle miles traveled, and a significant 44% reduction in the rear-end crash rate of large trucks. AEB also was associated with significant reductions—12% overall and 41% for rear-end crashes. Warnings were issued in 31% of rear-end crashes for FCW-equipped trucks. AEB intervened in 43% of rear-end crashes; about two thirds of these interventions involved autobrake activations. On average, speed was reduced by over half between the time of the intervention and impact for both systems. Observed reductions in same-direction sideswipe and roadway departure crashes per mile traveled were smaller in magnitude than those of rear-end crashes; these were consistent with other crash avoidance technologies suspected to be bundled with FCW/AEB in some cases, and very few front crash prevention interventions occurred in these types of crashes. Conclusions: FCW and AEB are effective countermeasures for crashes in which large trucks rear-end other vehicles. Large truck safety is expected to improve as new trucks are increasingly equipped with these systems. FCW has the advantage that some of these systems can be retrofitted to existing trucks, so benefits can be realized sooner and with less investment.

Field testing the applicability of motorcycle autonomous emergency braking (MAEB) during pre-crash avoidance maneuver

Objective Autonomous Emergency Braking (AEB) is a promising technology for crash avoidance or pre-crash impact speed reduction through the automatic application of braking force. Implementation of AEB technology on motorcycles (MAEB) is still problematic as its interaction with the rider may compromise the safety. In previous studies, MAEB interventions at low decelerations were shown to be easily manageable by common riders in straight line condition, but they were not previously tested in lateral maneuvers such as lane change and swerving, which are common in pre-crash situations. The objective of this paper is to assess the applicability of MAEB activation during lateral avoidance maneuver and to estimate its benefits in this scenario. Methods Field tests were carried out involving common riders as participants, using a test protocol developed on the experience of previous studies. The test vehicle was a sport-touring motorcycle equipped with an automatic braking system that could be activated remotely by researchers to simulate MAEB intervention. The motorcycle was equipped with outriggers to prevent capsizing. The Automatic Braking (AB) interventions using a nominal deceleration of 0.3 g were deployed at pseudo-random times in conditions of straight-line travel and a sharp lane-change maneuver emulating a pre-crash avoidance action. The straight-line trials were used as the reference condition for analysis. Results Thirty-one participants experienced AB interventions in straight-line and lane-change at an average speed of 44.5 km/h. The automatic braking was deployed in all the key phases of the avoidance maneuver. The system reached a deceleration of 0.3 g for a time of intervention of approximately 1 s. The participants were consistently able to control the vehicle during the automatic braking interventions and were always able to complete the lane-change maneuver. The speed reductions obtained with the AB interventions during lane change were very similar to those obtained in the straight-line conditions. Conclusions MAEB interventions with decelerations up to 0.3 g can be easily managed by motorcycle riders not only in straight-line conditions but also during an avoidance maneuver. Further investigations using higher deceleration values are now possible.

Model-Based Systemic Hazard Analysis Approach for Connected and Autonomous Vehicles and Case Study Application in Automatic Emergency Braking System

Model-Based Systemic Hazard Analysis Approach for Connected and Autonomous Vehicles and Case Study Application in Automatic Emergency Braking System

Automatic braking system using fuzzy logic method

This research is design an autonomous car control system that can reduce the rate of traffic accident. Car prototype is designed by using Fuzzy Logic Control. Fuzzy logic is as main component of artificial intelligence has significantly influence the design controlled system. Fuzzy logic system is integrated with the arduino Mega 2560 microcontroller as the central control car prototype. In this paper, the Fuzzy logic used Mamdani method and 28 rule base. The fuzzy system was made based on fuzzy mechanism which are fuzzification, inference and defuzzification. Fuzzy logic has a variable input and output variables. Input variables used in this research is the distance between car and obstacle; the speed of car prototype and the output variable is the brake angle. The test is done with 5 variations of distance that is 100 cm, 125 cm, 150 cm, 175 cm, and 200 cm and two variations of speed that are 54 cm/s, and 63 cm/s. The test results showed that the prototype of the car was able to stop with a range of 22 cm - 35 cm from obstacle. The highest deceleration value at the speed of 63 cm/s is - 49.22 cm/s2 and the accuracy of braking on a system that has been designed is between 99.91% -100%.

The future of the Autonomous Emergency Braking for Powered-Two-Wheelers: field testing end-users’ acceptability in realistic riding manoeuvres

Motorcycle Autonomous Emergency Braking system (MAEB), is a technology that introduces also on Powered-Two-Wheelers (PTWs) the autonomous braking, which is able to apply autonomously a braking force to reduce impact speed in emergency situations. This system was shown to be possibly effective in reducing numbers of deaths and serious injuries resulting from motorcycle crashes. However, its safe applicability on standard vehicles and the acceptability among end-users has still to be proven. The goal of the study presented in this paper is to assess the acceptability and the controllability of automatic braking events deployed in realistic riding manoeuvres. Field tests were conducted involving 55 common riders as participants on three test vehicles: a naked motorcycle, a sport touring motorcycle and three-wheels scooter. The automatic braking was tested in four riding manoeuvres (straight-lane, lane-change, slalom, and curve) deployed remotely by an investigator at a travelling speed of 35-50 km/h. The system was tested with the higher levels of interventions tested so far by common users and more than 1100 interventions were recorded. The results of this study will allow having a new understanding on the limits of MAEB system.

Analysis of the intervention of an anti-fall device in Motorcycle Autonomous Emergency Braking (MAEB) experimental tests

Motorcycle Autonomous Emergency Braking (MAEB) is a rider assistance that may soon play a role for the improved safety for Powered-Two-Wheeler users. Several studies were carried out to investigate the feasibility of automatic braking (AB) events. In a recent experiment, tests were conducted with volunteer participants to assess AB intervention under different riding conditions. The prototype vehicles were equipped with an anti-fall device (outriggers), the main object of this study, to ensure participants’ safety. The outriggers limit the roll angle of the motorcycle, thus preventing accidental capsize. Data from the sensors installed on the side arms were analyzed to identify ground contact events, for which a correlation with the driving style adopted by the participants has emerged. There was one case in which MAEB activation was followed by outriggers intervention. However, detailed analysis indicated that this could not be assimilated to an incipient fall. Our results show that the simple sensor setup was able to gather useful information for the experimental tests.

Intelligent Vehicle Automatic Stop-and-Go Task Based on Humanized Learning Control Model

The automatic stop-and-go task of intelligent vehicles can make the adaptive cruise control system achieve a full-speed range. However, the conventional design methods mostly focus on functional safety, without considering drivers’ behaviors, thereby leading to a poor driving experience. To improve the situation, a humanized learning control model is used instead of mechanical switching logic. Therefore, first, the common characteristics of human drivers with different driving styles are found by analyzing real drivers’ experiments. Then, the vehicle automatic starting function is designed based on iterative learning control with the fast Fourier transform for acceleration fitting. Next, the vehicle automatic braking function is designed based on dynamic time to collision. Finally, the simulation of the stop-and-go scenario is shown in CARSIM, and the real vehicle test is performed under the urban overpass driving condition. Results show that the proposed model can improve the humanization in the vehicle stop-and-go task.

A kriminalisztika alapkérdései és az önvezető járművek

Driving is already supported by a number of technical devices. Just think of the cruise control, lane change system, automatic deceleration, automatic parking system, electronic stability control, automatic emergency brakes or lane departure warning. Although the introduction of self-driving vehicles in Hungary still seems to be a distant future, it is already present in many countries (especially in the USA). There are countless fears and uncertainties about self-driving vehicles, but the benefits are undeniable. The introduction of self-driving vehicles raises a number of legal, technical, informatics and ethical issues. Of them, the study examines the advantages and disadvantages of introducing self-driving vehicles in the light of basic forensic issues, after clarifying ethical issues (see trolley- problem) and guilt issues. Within this, it is limited to traffic offenses (so the issue of cyber-terrorism related to self-driving vehicles, or even drug trafficking, is not the subject of the study). It has been found that some questions may make it more difficult to detect a crime (it is often difficult to answer the question who committed it). From the product manufacturer to the programmer, the operator or the user, many people can be held criminally liable. In comparison, thanks to the technical means and the network connection, we can count on ease of detection of the crimes with the help of self-driving vehicles in many issues. Arrived: 8 September 2020Accepted: 8 November 2020

Risk Mitigation Planning for Revenue Service Testing of Bus Automated Emergency Braking

In 2017, the Federal Transit Administration awarded Pierce Transit of Lakewood, WA, a $1.66 m grant for a bus collision avoidance and mitigation safety research and demonstration project. The project scope includes installation of an advanced technology package, the Pedestrian Avoidance Safety System (PASS) that uses light detection and ranging (LiDAR) sensors to trigger automated deceleration and braking. Thirty transit buses are being equipped with PASS and will be monitored using telematics to transmit and collect critical test data. The test plan includes collecting data while operating the buses in “stealth mode” with PASS detecting and logging events, but not activating brakes automatically or warning the drivers. At the conclusion of “stealth mode” operation, Pierce Transit will make a go/no-go decision on whether to activate PASS’s automatic deceleration and braking functionality for revenue service with passengers. This paper describes the risk mitigation process developed to determine if the system is safe enough to allow operation in revenue service. The process includes: broad stakeholder engagement, constituting an ad-hoc working group within Pierce Transit to advise executive management, development of decision-making criteria, consultation with state and federal officials on regulatory requirements and compliance, review of applicable standards and engineering test protocols, engineering consultations with the bus original equipment manufacturer, and road testing to simulate revenue service, collect data, and obtain feedback from drivers and maintainers.

Research on subjective evaluation method of automatic emergency braking (AEB) for passenger car

With the continuous development of automotive active safety technology, automatic emergency braking technology has been widely used, playing an important role in avoiding rear-end collisions or collisions with pedestrians and other traffic participants. This article analyzes the technical characteristics of the automatic emergency braking system, put forward subjective evaluation index, select vehicle-to-vehicle, vehicle -to-pedestrian and other typical scenes for automatic emergency braking subjective evaluation and actual vehicle verification, and constructed a subjective evaluation system for automatic emergency braking of passenger car.

Investigating the feasibility of motorcycle autonomous emergency braking (MAEB): Design criteria for new experiments to field test automatic braking

Autonomous Emergency Braking (AEB) was proved to be an effective and reliable technology in reducing serious consequences of road vehicles crashes. However, the feasibility in terms of end-users’ acceptability for the AEB for motorcycles (MAEB) still has to be evaluated. So far, only Automatic Braking (AB) activations in straight-line motion and decelerations up to 2 m/s2 were tested with common riders.This paper presents a procedure which provides comprehensive support for the design of new experiments to further investigate the feasibility of MAEB among end-users. Additionally, this method can be used as a reference for designing tests for other advanced rider assistance systems.•A comprehensive literature review was carried out to investigate previous findings related to MAEB. After that, a series of pilot tests using an automatic braking device on an instrumented motorcycle were performed.•The specifications for new AB experiments were defined (in terms of test conditions, participants requirements, safety measures, test vehicles and instrumentation).•A test protocol was defined to test the system in different riding conditions and with different AB working parameters. A proposal for the data analysis was presented.

Research on Virtual Test of Automobile Automatic Emergency Braking System on Ice-Snow Road

In view of the severe weather conditions in cold regions, the basic characteristics and braking distance of ice snow covered pavement are analyzed. This paper uses the PreScan/CarSim/Simulink software co-simulation method to test the automatic emergency braking (AEB) system on ice-snow roads. Through the appropriate adjustment of the time to collision (TTC) threshold, the car makes automatic emergency braking on the road with low friction coefficient system can achieve the effect of collision avoidance and injury reduction.

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...

Psychological factors of the transfer of control in an automated vehicle

Abstract In accordance with the requirements of the NHTSA guidelines on Level 3 automation, the comfortable control transition times is about 40 seconds. The data obtained so far are consistent with the assumption that the situation is better when drivers receive a warning about critical events than when they have to take over control unexpectedly. How these variables are shaped in the presence of distractors and what influences psychological factors have on these aspects remains unknown. For this purpose, a research experiment was developed in which control was taken over when the driver was additionally forced to perform the indicated activity (e.g. by looking away from the road), or when road conditions made it impossible to focus on the road. Psychological (temperament) and psychomotor variables (reaction time, hand–eye coordination) were controlled. The study was conducted on active road traffic participant drivers (N=95). Not only the time of taking control was analyzed, but also the way that may have a significant impact on road safety. The results revealed a significant influence of distractors on the manner control is taken over. In the conditions without distractors, the subjects were more likely to take over control than to cause automatic braking.

Collision Prediction and Optimal Trajectory Generation for Collision Avoidance Systems in Trackless Mobile Machines

The continued high number of fatalities associated with Trackless Mobile Machines (TMMs) in South Africa have led to the introduction of Collision Avoidance System (CAS) regulations in the Mine Health and Safety Act in 2015. This has lead to the profusion of technologically-immature CASs from third-party vendors, all of which are centered on automatic stopping and braking systems. These braking systems often result in trivial or ineffective solutions, proving costly to mining operations. The combination of braking and steering control in CASs may substantially increase the solution space and provide far safer and more efficient manoeuvres. A recursive non-linear collision prediction estimator and optimal trajectory generation model was developed to evaluate the potential contribution of the addition of steering to CASs. Three independent optimal trajectory generation models are proposed to compete against one another in an attempt to synthesize the safest, most predictable, and efficient trajectory. A deep reinforcement learning, lattice optimization and Monte Carlo hyper sampling path planning model’s trajectories are evlauated using the Earth Moving Equipment Safety Round Table (EMESRT) interaction scenarios. Initial results indicate increased CAS solution spaces in collision-avoiding scenarios, providing safer and more effective solutions in high velocity vehicle interactions.

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

Simulation-based Benefit Estimation of Automatic Emergency Braking in Vehicle to Pedestrian Collisions

Simulation-based Benefit Estimation of Automatic Emergency Braking in Vehicle to Pedestrian Collisions