Autonomous Braking Research Articles

Autonomous Braking System for Automobile Powered by Artificial Intelligence and Reinforcement Learning

Autonomous Braking System for Automobile Powered by Artificial Intelligence and Reinforcement Learning

Radar-Based Pedestrian and Vehicle Detection and Identification for Driving Assistance

The introduction of advanced driver assistance systems has significantly reduced vehicle accidents by providing crucial support for high-speed driving and alerting drivers to imminent dangers. Despite these advancements, current systems still depend on the driver’s ability to respond to warnings effectively. To address this limitation, this research focused on developing a neural network model for the automatic detection and classification of objects in front of a vehicle, including pedestrians and other vehicles, using radar technology. Radar sensors were employed to detect objects by measuring the distance to the object and analyzing the power of the reflected signals to determine the type of object detected. Experimental tests were conducted to evaluate the performance of the radar-based system under various driving conditions, assessing its accuracy in detecting and classifying different objects. The proposed neural network model achieved a high accuracy rate, correctly identifying approximately 91% of objects in the test scenarios. The results demonstrate that this model can be used to inform drivers of potential hazards or to initiate autonomous braking and steering maneuvers to prevent collisions. This research contributes to the development of more effective safety features for vehicles, enhancing the overall effectiveness of driver assistance systems and paving the way for future advancements in autonomous driving technology.

Effectiveness of the Autonomous Braking and Evasive Steering System OPREVU-AES in Simulated Vehicle-to-Pedestrian Collisions

This paper proposes a combined system (OPREVU-AES) that integrates optimized AEB and Automatic Emergency Steering (AES) to generate evasive maneuvers, and it provides an assessment of its effectiveness when compared to a commercial AEB system. The optimized AEB system regulates the braking response through a collision prediction model. OPREVU is a research project in which INSIA-UPM and CEDINT-UPM cooperate to improve driving assistance systems and to characterize pedestrians’ behavior through virtual reality (VR) techniques. The kinematic and dynamic analysis of OPREVU-AES is conducted using CarSim© software v2020.1. The avoidance trajectories are predefined for speeds above 40 km/h, which controls the speed and lateral stability during the overtaking and lane re-entry process. In addition, the decision algorithm integrates information from the lane and the blind spot detectors. The effectiveness evaluation is based on the reconstruction of a sample of vehicle-to-pedestrian crashes (INSIA-UPM database), using PCCrash© software v. 2013, and it considers the probability of head injury severity (ISP) as an indicator. The incorporation of AEB can avoid 53.8% of accidents, with an additional 2.5–3.5% avoided by incorporating automatic steering. By increasing the lateral activation range, the total avoidance rate is increased to 61.8–69.8%. The average ISP reduction is 65%, with significant reductions achieved in most cases where avoidance is not possible.

Recent Advances in Research into Jasmonate Biosynthesis and Signaling Pathways in Agricultural Crops and Products

Jasmonates (JAs) are phospholipid-derived hormones that regulate plant development and responses to environmental stress. The synthesis of JAs and the transduction of their signaling pathways are precisely regulated at multiple levels within and outside the nucleus as a result of a combination of genetic and epigenetic regulation. In this review, we focus on recent advances in the regulation of JA biosynthesis and their signaling pathways. The biosynthesis of JAs was found to be regulated with an autocatalytic amplification mechanism via the MYC2 regulation pathway and inhibited by an autonomous braking mechanism via the MYC2-targeting bHLH1 protein to terminate JA signals in a highly ordered manner. The biological functions of JAs mainly include the promotion of fruit ripening at the initial stage via ethylene-dependent and independent ways, the regulation of mature coloring via regulating the degradation of chlorophyll and the metabolism of anthocyanin, and the improvement of aroma components via the regulation of fatty acid and aldehyde alcohol metabolism in agricultural crops. JA signaling pathways also function in the enhancement of biotic and abiotic stress resistance via the regulation of secondary metabolism and the redox system, and they relieve cold damage to crops through improving the stability of the cell membrane. These recently published findings indicate that JAs are an important class of plant hormones necessary for regulating plant growth and development, ripening, and the resistance to stress in agricultural crops and products.

Robust Adaptive Autonomous Braking Control for Intelligent Electric Vehicles*

This paper presents a two-stage autonomous braking control for electric vehicles with preview information acquired by intelligent transportation systems (ITS) or connected and autonomous vehicle (CAV) technologies. The goal of the control method is firstly to plan a speed profile to comply with a target speed and a target residual distance to an upcoming deceleration event and then to track the planned speed profile by generating tracking-driven braking control torques, which maximally employ regenerative braking. Once deceleration circumstances are perceived, the intelligent reference speed planner computes a smooth speed profile by using a current vehicle speed, a target speed, and a target residual distance. For the braking control to robustly track the planned speed profile, a chattering-free sliding mode controller is designed on the interconnected vehicle and braking system model with braking system uncertainties and external disturbances, and a sufficient condition for the sliding mode gain is introduced. Also, adaptive update laws to compensate for the braking system uncertainties provide feed-forward control efforts for the tracking control inputs. To verify the effectiveness of the proposed control approach, the control methods are applied to a real commercial vehicle and the consecutive driving experiments with various deceleration circumstances illustrate a successful tracking-driven cooperative braking performance.

AUTOMATIC TEMPO CONTROL OF VEHICLE USING MULTI SENSORS

We have lost many precious lives daily due to road accidents. The main reasons are driver error, road situation, and vehicle condition. The system works to help drivers during emergencies and risks. The normal braking system is fully dependent on the driver will never prevent a collision in a sudden critical situation braking and the vehicle tends to crash. The autonomous braking system plays today a big role in accident prevention and the system is unfortunately only used in high cargo vehicles. The system also has speed limiter functions. It will decrease or increase vehicle speed depending on the obstacle distance from the moving vehicle to minimize damage or collision in an accident. The proposed algorithm is recovery braking and release braking based on the time to crash. The suggested design and system can be used in lower-class vehicles to reduce fatality using hybrid data and hybrid sensors. The goal of the research is to perceive the subject and stop the vehicle in an emergency.

Autonomous Braking System Using Linear Actuator

The most frequent cause of vehicle accidents (car, bike, truck, etc.) is the unexpected existence of barriers while driving. An automated braking system will assist and minimize such collisions and save the driver and other people’s lives and have a substantial influence on driver safety and comfort. An autonomous braking system is a complicated mechatronic system that incorporates a front-mounted ultrasonic wave emitter capable of creating and transmitting ultrasonic waves. In addition, a front-mounted ultrasonic receiver is attached to gather ultrasonic wave signals that are reflected. The distance between the impediment and the vehicle is determined by the reflected wave. Then, a microprocessor is utilized to control the vehicle’s speed depending on the detected pulse information, which pushes the brake pedal and applies the vehicle’s brakes extremely hard for safety. For work-energy at surprise condition for velocity 20 km/hr, the braking distance is 17.69 m, and for velocity 50 km/hr, the braking distance is 73.14.

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.

AN AUTONOMOUS BRAKING CONTROL SYSTEM FOR A 2017 YAMAHA GRIZZLY 700

Weed control is an important issue for environmental protection all around the world. Traditional hand weed control is laborious whereas chemical control is costly and a threat to the atmosphere. A chemical patch weed control system is an optimized system but lacks cheap technical equipment. This research outlines a design process and test of a braking system that can be applied during the designing of an autonomous braking system for a 2017 Yamaha Grizzly 700. The system is intended to be used as an autonomous weed chemical spraying. A bolt-on approach that did not require any manipulation of the stock, an internal braking system was followed to reduce the complexity and installation time of multiple systems. Three different types of autonomous braking system solutions were initially investigated, with the linear actuator solution being decided on through the assistance of a weighted decision matrix. The system was designed around a 30 kg hand force; however, a spare actuator of approximately 20 kg of force was repurposed and used instead. Finite element analysis concluded that all major components within the proposed system were suitable for a lifetime of at least 1,000,000 cycles with a mild steel yield stress failure criterion of 370 MPa. A stationary test for the system was conducted to determine the success of the system, which pushed the brake lever approximately 25% of its disengaged handlebar to lever length. The resulting system met the requirements of the expectation and could be used to apply the ATV’s brakes autonomously while retracting the gear interlocking mechanism enough to change gears.

Automatic Braking System with Bumper Actuation

For most people nowadays, driving is a common activity. Technology has undergone significant modifications, resulting in an increase in speed. However, high speed contributes to traffic accidents. When the driver is not attentive, ordinary braking is insufficient to prevent accidents. The braking system must be improved further in order to brake a vehicle when the driver is unable to do so, which may necessitate the use of an automatic braking system. The vehicle may brake without the driver's assistance using this autonomous braking technology. An automated braking system is an important aspect of car safety technology. It's a sophisticated system that's designed to avoid colliding with another vehicle or an obstacle of some sort. These systems use sensors like radar, video, infrared, and ultrasonic to scan for potential obstacles in front of the car and then use brake control to avoid a collision if the object is found.

Modelling and simulation automatic braking systems for vehicles

An automatic braking system is an important future step in attending the autonomous vehicle. This system increases the active safety of a vehicle, significantly reducing road accidents, and lowering the effects of accidents. The paper will present the steps that will develop the autonomous braking systems in conformity with the Society of Automotive Engineers (SAE) from the USA. As well the paper will present the modelling and simulating of the automatic braking systems of a vehicle from the middle class using LMS Amesim software.

An improved automated braking system for rear-end collisions: A study based on a driving simulator experiment

Introduction: To assist drivers in avoiding rear-end collisions, many early warning systems have been developed up to date. Autonomous braking technology is also used as the last defense to ensure driver’s safety. Method: By taking the accuracy and timeliness of automatic system control into account, this paper proposes a rear-end Real-Time Autonomous Emergency Braking (RTAEB) system. The system inserts brake intervention based on drivers’ real-time conflict identification and collision avoidance performance. A driving simulator-based experiment under different traffic conditions and deceleration scenarios were conducted to test the different thresholds to trigger intervention and the intervention outcomes. The system effectiveness is verified by four evaluation indexes, including collision avoidance rate, accuracy rate, sensitivity rate, and precision rate. Results: The results showed that the system could help avoid all collision events successfully and enlarge the final headway distance, and a TTC threshold of 1.5 s and a maximum deceleration threshold of −7.5 m/s2 could achieve the best collision avoidance effect. The paper demonstrates the situations that are more inclined to trigger the RTAEB (i.e., a sudden brake of the leading vehicle and a small car-following distance). Moreover, the study shows that driver characteristics (i.e., gender and profession) have no significant association with system trigger. Practical Applications: The study suggests that development of collision avoidance systems design should pay attention to both the real-time traffic situation and drivers’ collision avoidance capability under the present situation.

Autonomous Braking System: for Automobile Use

Road fatality and injury are a worldwide issue in the transportation industry. Road traffic accidents are becoming increasingly significant due to higher mortality, injury, and disability across the world, particularly in developing and transitional economies. Eighty-five percent of the total road traffic fatalities occur in developing nations, with Asia-Pacific accounting for roughly half of them. A variety of factors influence road safety, including technological, physical, social, and cultural factors. The purpose of this research was to design an autonomous braking system (AuBS). Using the Adaptive Neuro-Fuzzy Intelligent System (ANFIS), a DC motor, sensors, and SAuBS have been developed to customize the traditional hydraulic braking system. The genetic algorithm has been developed to simulate the fundamental characteristics of the automotive braking system. The AuBS system goal is to slow the car without the driver's help infrequent braking when the vehicle is moving at slower speeds. When the ANFIS performance is compared to that of the AuBS model, it is discovered that the ANFIS performs roughly 15% better.

Mode Switching Control Using Lane Keeping Assist and Waypoints Tracking for Autonomous Driving in a City Environment

This paper proposes a mode switching supervisory controller for autonomous vehicles. The supervisory controller selects the most appropriate controller based on safety constraints and on the vehicle location with respect to junctions. Autonomous steering, throttle and deceleration control inputs are used to perform variable speed lane keeping assist, standard or emergency braking and to manage junctions, including roundabouts. Adaptive model predictive control with lane keeping assist is performed on the main roads and a linear pure pursuit inspired controller is applied using waypoints at road junctions where lane keeping assist sensors present a safety risk. A multi-stage rule based autonomous braking algorithm performs stop, restart and emergency braking maneuvers. The controllers are implemented in MATLAB® and Simulink™ and are demonstrated using the Automatic Driving Toolbox™ environment. Numerical simulations of autonomous driving scenarios demonstrate the efficiency of the lane keeping assist mode on roads with curvature and the ability to accurately track waypoints at cross intersections and roundabouts using a simpler pure pursuit inspired mode. The ego vehicle also autonomously stops in time at signaled intersections or to avoid collision with other road users.

Effects of an integrated collision warning system on risk compensation behavior: An examination under naturalistic driving conditions

Collision warning systems can improve traffic safety, while their safety benefit may be lessened due to improper risk compensation or system misuse. There are limited studies of advanced safety systems increasing unexpected risky driving behavior, especially with adolescent drivers. This study is designed to address this research gap in two main areas: 1) it seeks to examine whether and how the introduction of advanced driver-assistance systems influences drivers’ risk compensation behavior (e.g., increase of hard braking frequency), and 2) it investigates key factors (e.g., distraction) that contribute to changes in hard braking frequency during driving for both teen and adult drivers. Naturalistic driving data from two previous studies were analyzed in this study with two methods: a hierarchical logistic regression model was used to evaluate the effects of an integrated collision warning system on hard braking behavior, while a Random forests algorithm was applied to model hard braking behavior and to rank the contributing factors by calculating the importance scores. No statistical evidence was observed that the integrated collision warning system significantly changed the likelihood of hard braking for teen or adult drivers. Other factors like distraction, especially visual-manual distraction, had the largest impact on the hard braking behavior, followed by speeding and roadway segments (i.e., at intersections or not). Short time-headways and driving in high-density traffic significantly increased the likelihood of hard braking. Furthermore, the rate of hard braking behavior on surface roads was much higher than on highways, as expected. Compared with straight road segments, hard braking behavior was less likely to occur on curve roads. This study applied an analytical strategy by using both machine learning and statistical analysis methods to achieve high model accuracy and facilitate inference concerning the relationships among variables. Findings in this study can help to improve the design of integrated collision warning systems and the use of autonomous braking systems, and to apply appropriate analysis methods in understanding teen drivers’ behavior changes with those safety systems.

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 Relationship between Comfort and Body Behavior during Autonomous Braking

Analysis of the Relationship between Comfort and Body Behavior during Autonomous Braking

An Accurate GNSS-Based Redundant Safe Braking System for Urban Elevated Rail Maglev Trains

The association of elevated rail structures and Maglev (magnetic levitation) trains is a promising alternative for urban transportation. Besides being cost-effective in comparison with underground solutions, the Maglev technology is a clean and low-noise mass transportation. In this paper, we propose a low-cost automatic braking system for Maglev trains. There is a myriad of sensors and positioning techniques used to improve the accuracy, precision and stability of train navigation systems, but most of them result in high implementation costs. In this paper, we develop an affordable solution, called Redundant Autonomous Safe Braking System (RASBS), for the MagLev-Cobra train, a magnetic levitation vehicle developed at the Federal University of Rio de Janeiro (UFRJ), Brazil. The proposed braking system employs GNSS (Global Navigation Satellite System) receivers at the stations and trains, which are connected via an ad-hoc wireless network. The proposed system uses a cooperative error correction algorithm to achieve sub-meter distance precision. We experimentally evaluate the performance of RASBS in the MagLev prototype located at the campus of UFRJ, Brazil. Results show that, using RASBS, the train is able to dynamically set the precise location to start the braking procedure.

Study of the Emergency Braking Test with an Autonomous Bus and the sEMG Neck Response by Means of a Low-Cost System.

Nowadays, due to the advances and the increasing implementation of the autonomous braking systems in vehicles, the non-collision accident is expected to become more common than a crash when a sudden stop happens. The most common injury in this kind of accident is whiplash or cervical injury since the neck has high sensitivity to sharp deceleration. To date, biomechanical research has usually been developed inside laboratories and does not entirely represent real conditions (e.g., restraint systems or surroundings of the experiment). With the aim of knowing the possible neck effects and consequences of an automatic emergency braking inside an autonomous bus, a surface electromyography (sEMG) system built by low-cost elements and developed by us, in tandem with other devices, such as accelerometers or cameras, were used. Moreover, thanks to the collaboration of 18 participants, it was possible to study the non-collision effects in two different scenarios (braking test in which the passenger is seated and looking ahead while talking with somebody in front of him (BT1) and, a second braking test where the passenger used a smartphone (BT2) and nobody is seated in front of him talking to him). The aim was to assess the sEMG neck response in the most common situations when somebody uses some kind of transport in order to conclude which environments are riskier regarding a possible cervical injury.

A Decision-Making Strategy for Vehicle Autonomous Braking in Emergency via Deep Reinforcement Learning

Autonomous braking through vehicle precise decision-making and control to reduce accidents is a key issue, especially in the early diffusion phase of autonomous vehicle development. This paper proposes a deep reinforcement learning (DRL)-based autonomous braking decision-making strategy in an emergency situation. Three key influencing factors, including efficiency, accuracy and passengers' comfort, are fully considered and satisfied by the proposed strategy. First, the vehicle lane-changing process and the braking process are analyzed in detail, which include the critical factors in the design of the autonomous braking strategy. Second, we propose a DRL process that determines the optimal strategy for autonomous braking. Particularly, a multi-objective reward function is designed, which can compromise the rewards achieved of different brake moments, the degree of the accident, and the comfort of the passenger. Third, a typical actor-critic (AC) algorithm named deep deterministic policy gradient (DDPG) is adopted for solving the autonomous braking problem, which can improve the efficiency of the optimal strategy and be stable in continuous control tasks. Once the strategy is well trained, the vehicle can automatically take optimal braking behavior in an emergency to improve driving safety. Extensive simulations validate the effectiveness and efficiency of our proposal in terms of learning effectiveness, decision-making accuracy and driving safety.