Braking Maneuvers Research Articles

Scooter Tire Pressure Influence on a Hard Braking Manoeuvre

The mortality of road accidents quadruples when, in addition to motorists, a motorcyclist is also involved. The higher mortality is influenced by the greater exposure of the rider to injuries during the impact. This problem can be mitigated by reducing, even slightly, the impact’s speed. Bench tests carried out by previous studies showed a considerable influence of the inflation pressure on the properties of motorcycle tires. It could therefore have significant effects on the deceleration achievable under braking. This study aimed to evaluate the effect of inadequate tire inflation pressure (excessive or insufficient) on the average deceleration achievable during a hard braking manoeuvre performed only with the rear wheel and with ABS intervention. Experimental tests were carried out on a Piaggio Beverly S 300 in a controlled road environment closed to traffic. Different inflation pressures of the rear braking tire were tested during multiple runs. The data were acquired through a smartphone used as an IMU. At the nominal pressure of 2.4 bar the measured average deceleration was 3.69 m/s2 (SD = 0.12 m/s2). When increasing the pressure to 3.0 bar and decreasing it to 1.8 bar the average deceleration was reduced respectively to 3.67 m/s2 (-0.8%, SD = 0.10 m/s2) and 3.59 m/s2 (-3.0%, SD = 0.16 m/s2). The reduction in achievable deceleration was partly mitigated by the reduced load transfer on the rear tire, which partially compensated for the reduction of the braking force coefficient. The results showed that a moderate inflation pressure variation on the rear braking tire modestly influences the achievable average deceleration.

Shock Absorber Leakage Impact on Aircraft Lateral Stability During Ground Handling Maneuvers

Aircraft braking maneuvers are safety-critical on-ground motions that exhibit complex dynamics and significant dependence on system operating conditions. The fundamental interface between the aircraft and the ground is the landing gear. Among the landing gear components, the shock absorbers may be subject to gas leakage during their lifetime, which is an anomaly that could compromise the lateral stability properties of the aircraft on the operating regimes found during braking maneuvers. In this paper, an explicit link is established between main landing gear shock absorber leakage and aircraft lateral stability. To investigate lateral stability, a high-fidelity multibody nonlinear aircraft simulator is developed in a MATLAB/Simulink framework and validated against experimental data. To generate insight into the problem and to quantify shock absorber leakage impact on aircraft lateral stability, two simple but descriptive analytical models are also developed, each one on a different operating mode of the system. The analysis of the models reveals that shock absorber leakage can have a significant effect on aircraft lateral stability, especially at high velocities and highly damped nose wheel steering conditions. The models developed in this work may be used by aircraft control system designers to come up with more effective lateral stability controllers in the event of main landing gear shock absorber leakage.

Decision Making for Automated Driving by Reachability of Parameterized Maneuvers

We consider a decision making system for automated driving that has the objective of determining what maneuvers are feasible for the current vehicle, route, and traffic conditions. For the maneuvers determined to be feasible, a motion planner can then generate the trajectories that achieve the corresponding goals, without the risk of wasting computations in searching for a trajectory of an impossible maneuver. We solve the decision making problem by constructing backward reachable sets for goals and collision areas, based on maneuvers that are generated by dynamical models with decision parameters. Online, we only need to check the existence of parameter values that provide membership of the state-parameter vector in a goal reachable set, and non-membership in all collision reachable sets, which entails simple and fast computations. We evaluate the method in scenarios involving lane change and braking maneuvers.

Active Transfer Prototypical Network: An Efficient Labeling Algorithm for Time-Series Data

The paucity of labeled data is a typical challenge in the automotive industry. Annotating time-series measurements requires solid domain knowledge and in-depth exploratory data analysis, which implies a high labeling effort. Conventional Active Learning (AL) addresses this issue by actively querying the most informative instances based on the estimated classification probability and retraining the model iteratively. However, the learning efficiency strongly relies on the initial model, resulting in the trade-of between the size of the initial dataset and the query number. This paper proposes a novel Few-Shot Learning (FSL)-based AL framework, which addresses the trade-of problem by incorporating a Prototypical Network (ProtoNet) in the AL iterations. The results show an improvement, on the one hand, in the robustness to the initial model and, on the other hand, in the learning efficiency of the ProtoNet through the active selection of the support set in each iteration. This framework was validated on UCI HAR/HAPT dataset and a real-world braking maneuver dataset. The learning performance significantly surpasses traditional AL algorithms on both datasets, achieving 90% classification accuracy with 10% and 5% labeling effort, respectively.

Баллистическая оценка полетов серии «Аполлон» к Луне

Materials from the US sources on the Apollo program were considered, and the presented data were evaluated from the point of view of external ballistics equations, flights of spacecraft to the Moon and their return to the Earth. The evaluation made it possible to determine inconsistency of the data (mass characteristics) in different documents relating to one object in the Apollo 11 mission. Inconsistencies were also found in the ballistic schemes for launching into the departure trajectory to the Moon, mooning, undocking and docking in the lunar orbit, braking maneuver to enter the lunar orbit, acceleration maneuver for returning to the Earth and braking maneuver for landing on the Earth’s surface. Analysis of the lunar missions’ documents formed the basis to ask questions that were not previously raised in such a formulation, and no answers were given to them. In particular, significant discrepancies in the initial data were found in the official documents on the US lunar missions. Reference orbit altitude before the second activation of the Saturn-5 3rd stage, the first space velocity at the Florida peninsula inclination and the second space velocity seem to be strange. Significant doubts are caused by acceleration schedule, projection of the flight path to the Earth and remoteness of the first stage impact area from the launch pad. In terms of the F-1 engines of the Saturn-5 launch vehicle first stage, thrust indicators of one engine and the total thrust of the first stage are not consistent. A conclusion is made that at present it is almost impossible to put into practice some of the maneuvers demonstrated in the US documents.

Formally Robust and Safe Trajectory Planning and Tracking for Autonomous Vehicles

In this paper, a safe trajectory planning and tracking algorithm for autonomous vehicles is proposed. Specially, the safety problem considers the geometric constraints including the obstacle avoiding and the road side constraints, and the non-convex input constraints defined from the sideslip angle of the wheels and input boundedness. Control barrier function (CBF) is adopted to deal with the state and input constraints and generate nominal trajectory. For this purpose, the nominal dynamics of the autonomous vehicle is defined as the virtual dynamics, from which the CBF safety certificates are derived. By constructing appropriate feedback control, the tracking error of the actual trajectory can be bounded into a tube, which guarantees the geometric safety of the actual vehicle. Two safety certificates, the bearing and the distance safety certificates are derived for multiple-obstacle avoidance. In order to deal with the non-convex input constraints, a safe braking maneuver is carefully considered. The feasible initial velocity set for safe braking is proposed as a part of state constraints. The feasibility and the safety of the overall system is proved. The algorithm to synthesis the CBF certificates for multiple-obstacle avoidance, and the input constraints is proposed. Simulation results from an autonomous vehicle including disturbances demonstrate the feasibility of the algorithm. The software implementation of the proposed algorithm was developed in C++ intended for real-world testing.

Testing of human reactions during braking manoeuvres combining a physical test bed with virtual reality

For the development of restraint systems, the initial posture and kinematics of the occupants immediately before the crash are essential. To predict the behaviour of vehicle occupants in pre-crash manoeuvres, volunteer tests under controlled boundary conditions to calibrate and validate active human body models are needed. The challenge in such experiments is to evoke a natural behaviour of the volunteers. The aim of this study was to investigate the potential of distractions via virtual reality (VR) in volunteer tests to study pre-crash kinematics. For this purpose, it was investigated if the volunteers are able to get immersed into the virtual reality using head-mounted displays to an extent in which they forget the laboratory environment and feel as if they are really sitting in an autonomous vehicle. A total of 27 volunteers took part in the study, and 10 different trials were carried out with each of them. In the baseline trial, no VR was displayed and the volunteers were made aware that the sled is going to start moving by a countdown. In trials 3–10, the volunteers were instructed to perform various tasks in virtual reality, which we have developed for this specific purpose. The physical platform on which they were sitting on began to move mimicking a braking maneuver as they performed a task. The peak forward head excursions resulting from the different trials were compared and showed significant differences between the baseline trial and the trials where they were distracted with VR. In contrast to our expectations, peak forward head excursions were overall significantly lower for the volunteers distracted by VR compared to the baseline trial. However, volunteers, who had significantly lower peak forward head excursions in the baseline trials showed higher peak forward head excursions in the VR trials comparable in magnitude to the other volunteers. All the volunteers answered in the questionnaire that their experience had been good and they were willing to participate in the study again. Further research is needed to understand the effect of the performed activities on the volunteer muscle activation and head excursions.

Simulation of the thermo-mechanical behaviour of tread braked railway wheels by means of a 2D finite element model

The friction heat generated at the wheel-shoe interface during tread braking operations leads to a temperature rise of the wheel tread surface. Fast cooling as well as residual thermal stresses and strains can cause damages to the wheel surface, thus negatively affecting the vehicle dynamic behaviour and strongly reducing the wheel life. Modelling the thermo-mechanical interaction of the wheel-block pair is thus of paramount importance to improve the reliability of braking manoeuvres. The paper shows the development of a decoupled thermo-mechanical model and its implementation in ANSYS Mechanical APDL, which can compute the contact pressure at the wheel-shoe interface and the resulting wheel temperature field produced during braking operations, considering friction heating, air convection cooling and the rail chill effect.

Electrothermal Multicriteria Comparative Analysis of Two Competitive Powertrains Applied to a Two Front Wheel Driven Electric Vehicle during Extreme Regenerative Braking Operations

The powertrain performance in an electric vehicle is fully dependent on the electrical and thermal constraints of the static converters ensuring the power transfer taking place between the energy storage systems and the electromechanical machines. These constraints depend on the architectures of the power converters, and their control strategies. Particularly, the maximal limits are reached in maneuvers such as hard regenerative braking circumstances. Indeed, braking recovery is a critical phase in the vehicle’s operation, and its duration and intensity may strongly impact the vehicle’s battery behavior or integrated hybrid storage system. The innovative objective of the paper is to propose an electrothermal multicriteria comparative study based on electrical and thermal criteria for two competitive powertrains. These semi-active power configurations (a 3-level DC/DC converter-based, and a Z-source converter-based) are implemented in a two-front wheel driven electric vehicle during extreme regenerative braking conditions. Open-loop and closed-loop controls were implemented in the Z-source using the maximal constant boost control with 3rd harmonic injection modulation technique. We considered two paralleled IGBT modules instead of the single shoot-through structure. Our approach is based on simulation during an extreme braking maneuver leading to heavy repercussions on the overall powertrain system. The aim is to investigate the challenging structure of the Z-source. Results showed that the proposed 3-level DC/DC-based topology has better performances in terms of power losses, efficiency, thermal behavior, and electromagnetic interference.

Optimal brake force distribution in motorcycle for straight-line braking

Commercial motorcycles and scooters incorporate independent mechanisms for brake actuation. But due to the significant contribution of dynamic load transfer in the event of braking, there is a need to distribute brake force and apply this force combined instead of independent actuation. This paper presents an analysis of the available braking options and their effect on the stability of the motorcycle. A simple motorcycle model was developed in Simulink for analyzing the basic phenomenon that occurs during braking and considering the testing requirements as per the standard for straight-line braking. The braking maneuver is analyzed along with its effect on sprung and unsprung mass over the range of possible deceleration rates for independent braking (only front or only rear) and combined braking. Finally, the strategy for optimal distribution of braking force is applied and its effect on the suspension system is analyzed. It is found that with the application of the proposed brake strategy higher braking force and deceleration can be achieved along with reduced suspension travel compared to the independent braking strategy.

Measurement and analysis of low‐acceleration and long‐duration longitudinal events using delivery van

This study investigates longitudinal acceleration events during freight transportation characterized as low‐acceleration and long‐duration using delivery van type vehicles. In the past several decades, there has been an increase in shipments requiring only single or small pallet load quantities and mixed palletized unit loads comprised of different goods. These loads are often transported in delivery vans without load securing devices, increasing the risk of product loss and damage due to load failures resulting from unit loads shifting. A field data acquisition system was used to observe and record the random acceleration events from five vehicles for 5 days, explicitly targeting the vehicles' braking and acceleration manoeuvres. The study aimed to understand the physical phenomenon and provide new information that can be used during preshipment tests to prevent damage to goods and ensure unit load integrity is maintained throughout the supply chain. The events were statistically analysed to understand their probability of occurrence, severity level, and quantify critical parameters such as event rise and hold times. For the braking manoeuvre, the statistical mean of average deceleration was 0.25 g with a corresponding rise and hold duration of 0.83 and 1.27 s, respectively. During the vehicle's acceleration manoeuvre, the statistical mean of average acceleration was 0.29 g with a rise and hold time of 1.29 and 1.39 s, respectively. Utilizing the field data, composite profiles were developed, and these profiles were compared to the currently available test procedures and previous results of other studies.

Over 60,000 km in a year: remotely collecting large-volume high-quality data from a logistics truck

After the first successful large-scale demonstration of eleven self-driving vehicles at the DARPA Urban Challenge in 2007, research results from the competing teams found their way into advanced driver systems (ADAS) that support typical driving tasks like adaptive cruise control and semi-automated parking. However, as of today, SAE Level 4 vehicles are not commercially available yet, which would allow the driver to be inattentive for longer periods. Hence, SAE Level 3, which represents partial automation yet continuously monitored by a human operator, may provide a step towards a viable SAE Level 4 product especially for commercial freight logistics. However, large amounts of data from such freight operations is needed to study the unique challenges in such use cases. In this paper, we present the system and software architecture of an end-to-end data logging solution, which is capable of recording large volumes of high-quality data. The system is installed in a commercial truck that is in daily operation by a logistics company and hence, the recorded data is only accessible remotely (i.e., over-the-air). We report about the fail-safe system design, initial findings from over one year of operation, as well as our lessons learned. During its first year of operation, the truck was used for 210 days by the logistics company, out of which 193 days were logged resulting in more than 4.5 TB of data from five cameras, two GNSS–IMU sensors, and six on-board vehicle controller area networks (CAN) busses. We demonstrate the value of the proposed end-to-end approach for traffic and driver behavior research by analyzing the uploaded data in the cloud to spot critical events such as unexpected harsh braking maneuvers caused by lane merging operations.

Using Active Seat Belt Retractions to Mitigate Motion Sickness in Automated Driving

The introduction of automated-driving functions provides passengers with the opportunity to engage in non-driving related tasks during the ride. However, this benefit might be compromised by an increased incidence of motion sickness. Therefore, we investigated the effectiveness of active seat belt retractions as a countermeasure against motion sickness during inattentive automated driving. We hypothesized that seat belt retractions would mitigate motion sickness by supporting passengers to anticipate upcoming braking maneuvers, by actively tensioning their neck muscles and, thereby, reducing the extent of forward head movement while braking. In a motion base driving simulator, 26 participants encountered two 30 min automated drives in slow-moving traffic: one drive with active seat belt retractions before each braking maneuver and a baseline drive without. The results revealed that there was no difference in perceived motion sickness between both experimental conditions. Seat belt retractions resulted in an increased activity of the lateral neck muscles and supported drivers to anticipate braking maneuvers. However, at the same time, the retractions led to an increased magnitude of head movement in response to braking. This research lays the groundwork for future research on active seat belt retractions as a countermeasure against motion sickness and provides directions for future work.

Motorcycle simulator subjective and objective validation for low speed maneuvering

The use of driving simulators for training and for development of new vehicles is widely spread in the automotive industry. In the last decade, a few motorcycle riding simulators have been developed for similar purposes, with focus on maneuvering at high speed. This article presents the subjective and objective evaluation of a motorcycle riding simulator specifically for low speed longitudinal and lateral maneuvering, between 0 and 10 ms–1. An experiment was conducted with and without platform motion, focusing on three maneuvers: acceleration from standstill, braking to standstill and turning at constant speed. Participants briefly evaluated the fidelity of the simulator after each maneuver and more extensively after each motion condition. Behavioral fidelity was evaluated using experimental data measured on an instrumented motorcycle. Overall, the results show that the participants could reproduce the selected maneuvers without falling or losing balance, reporting a sufficient level of simulator realism. In terms of subjective fidelity, platform motion had a positive effect on simulator presence, significantly increasing the feeling of being involved in the virtual environ0ment. In terms of behavioral fidelity, the comparison between the simulator and experimental results shows good agreement, with a limited positive influence of motion for the braking maneuver, which indicates that for this maneuver the use of motion is beneficial to reproduce the real-life experience and performance.

Active motor damping strategy for driveline vibrations of a hybrid electric vehicle during ABS operation

Torsional drivetrain vibrations in hybrid and electric drivetrains, which occur during traction and braking control, are commonly encountered. This problem occurs mainly due to the weak damping characteristic of the hybrid/electric drivetrain and the fast time response characteristics of the electric motor. An active motor damping (AMD) control algorithm for a hybrid electric vehicle, considering ABS braking maneuvers on different road conditions, is developed in this work. The control problem is structured such that the control objective is disturbance rejection against the estimated brake torque signal received from the ABS module. Communication delay between the ABS and motor control module is handled within the control strategy. State feedback control strategy is used with the linearized version of the non-linear state-space equations together with an Extended Kalman filter. Gear backlash is taken into account without the need to estimate the mode of backlash. In the development of the controller, state feedback gains are set such that the ABS functionality on tire slip regulation is not altered. Existing studies in the literature feedback only the angle of twist and axle wrap angular speed for damping the vibrations. The structure of the controller in this study is different in that it estimates and feeds back tire slip, in addition to these two. Simulation results show the effectiveness of the controller in reducing the angle of twist without deteriorating ABS tire slip control and braking performance of the vehicle.

Driver-initiated take-overs during critical braking maneuvers in automated driving – The role of time headway, traction usage, and trust in automation

Transitions of vehicle control between automated vehicle and driver remain a necessity in the near future. Most research focuses on system-initiated transitions of control. However, drivers may also actively decide to take over without being prompted by the automation. The present study aims to uncover predictors of such driver-initiated take-overs in automated driving and to examine their impact on traffic safety.We conducted two driving simulator studies with a total of 100 participants examining whether trust in automation and the criticality of the driving situation predict driver-initiated take-overs during highly dynamic braking maneuvers. Trust was varied via automation reliability in a prior induction phase, while criticality was manipulated via different levels of time headway (THW) and traction usage (TU). Potential limitations of study 1 concerning trust induction and predictor operationalization were addressed and eliminated in study 2.Results of both studies show that drivers’ trust in automation and THW affected the probability of driver-initiated take-overs. TU affected take-over probability only in interaction with THW and trust. Moreover, TU was associated with rear-end collisions.Our experiments demonstrate that driver-initiated take-overs in automated driving do occur and that drivers’ subsequent behavior may impair traffic safety. A better understanding of driver-initiated take-overs helps to increase the safety potential of automated vehicles, e.g., by designing assistance systems which will support drivers who initiate a take-over under critical, highly dynamic conditions.

Influence of road profiles and truck braking on the dynamic load transfer to the pavement

ABSTRACT During the braking manoeuvre of a vehicle on a pavement, the mechanics associated with load transfer is complex due to the presence of slip, load shift between the front and rear axles, and development of longitudinal forces. The road profiles, braking intensity, truck mass, and speed during braking play a critical role here. This investigation reports an attempt to collect such data. A unique Hardware-in-Loop set-up was integrated with vehicle dynamics software, TruckMaker, to perform a realistic braking manoeuvre. Three truck loadings were chosen from the axle load survey conducted in the field (unladen, laden, and overloaded). Three ISO road profiles (A, B, C) generated using the power spectral density approach, three initial speeds (30, 50, and 80 km/h), and three braking intensities (Partial (10%), Partial (45%), and Complete (100%)) were chosen for the simulation. In total 81 simulations were performed considering all the above-said factors. From the analysis of wheel slip values, it was identified that the wheels of unladen trucks lock during complete braking, irrespective of the speeds and road profiles. The complete braking manoeuvre for all vehicle types showed more load shift from the rear axle to the front axle compared to partial braking manoeuvres. Also, the unladen trucks were found to transfer more percentage (>50%) of their rear axle load to the front axle during braking. The maximum longitudinal force developed at the tyre-pavement interface was found to be independent of the loading conditions during complete braking.

A Reinforcement Learning Approach for Enacting Cautious Behaviours in Autonomous Driving System: Safe Speed Choice in the Interaction With Distracted Pedestrians

Driving requires the ability to handle unpredictable situations. Since it is not always possible to predict an impending danger, a good driver should preventively assess whether a situation has risks and adopt a safe behavior. Considering, in particular, the possibility of a pedestrian suddenly crossing the road, a prudent driver should limit the traveling speed. We present a work exploiting reinforcement learning to learn a function that specifies the safe speed limit for a given artificial driver agent. The safe speed function acts as a behavioral directive for the agent, thus extending its cognitive abilities. We consider scenarios where the vehicle interacts with a distracted pedestrian that might cross the road in hard-to-predict ways and propose a neural network mapping the pedestrian&#x2019;s context onto the appropriate traveling speed so that the autonomous vehicle can successfully perform emergency braking maneuvers. We discuss the advantages of developing a specialized neural network extension on top of an already functioning autonomous driving system, removing the burden of learning to drive from scratch while focusing on learning safe behavior at a high-level. We demonstrate how the safe speed function can be learned in simulation and then transferred into a real vehicle. We include a statistical analysis of the network&#x2019;s improvements compared to the original autonomous driving system. The code implementing the presented network is available at <uri>https://github.com/tonegas/safe-speed-neural-network</uri> with MIT license and at <uri>https://zenodo.org/communities/dreams4cars</uri>.

Nonlinear Control Design for Regenerative and Hybrid Antilock Braking in Electric Vehicles

The use of regenerative braking and recent electrohydraulic or electromechanical brake-by-wire systems with enhanced dynamics enables improved antilock braking control performance in electric vehicles. However, the possible benefit is often limited by suboptimal control architectures with separate distributed controllers for electric motor (EM) and friction brake torque requests. Furthermore, oversimplifications in synthesis models for brake control design constrain the achievable performance. Instead, to date, threshold-based algorithms are widely used in modern production vehicles despite having been designed for slower hydraulic brake systems originally. To exploit the full potential, this work proposes a continuous nonlinear control design using input–output linearization for robust wheel speed tracking control. The design uses a unified controller for the redundant actuators. In addition, it explicitly considers drivetrain oscillations induced by regenerative braking using onboard EMs in the synthesis model. The stability for the resulting zero dynamics is shown through the Lyapunov analysis. The nonlinear controller is augmented by a subsequent control allocation for splitting the control effort on the redundant actuators. The allocation design ensures consistent control performance for both regenerative braking and hybrid braking while simultaneously aiming for high-energy recovery. The overall antilock braking control design is implemented in an electric test vehicle. Its tracking performance, disturbance attenuation, and robustness are experimentally validated through various emergency braking maneuvers on different road surfaces.

Energy Management Strategy in 12-Volt Electrical System Based on Deep Reinforcement Learning

The increasing electrification in motor vehicles in recent decades can be attributed to higher comfort and safety demands. Strong steering and braking maneuvers reduce the vehicle’s electrical system voltage, which causes the vehicle electrical system voltage to drop below a critical voltage level. A sophisticated electrical energy management system (EEMS) is needed to coordinate the power flows within a 12-volt electrical system. To prevent the voltage supply from being insufficient for safety-critical consumers in such a case, the power consumption of several comfort consumers can be reduced or switched off completely. Rule-based (RB) energy management strategies are often used for this purpose, as they are easy to implement. However, this approach is subject to the limitation that it is vehicle-model-specific. For this reason, deep reinforcement learning (DRL) is used in the present work, which can intervene in a 12-volt electrical system, regardless of the type of vehicle, to ensure safety functions. A simulation-based study with a comprehensive model of a vehicle electric power system is conducted to show that the DRL-based strategy satisfies the main requirements of an actual vehicle. This method is tested in a simulation environment during driving scenarios that are critical for the system’s voltage stability. Finally, this is compared with the rule-based energy management system using actual vehicle measurements. Concluding measurements reveal that this method is able to increase the voltage at the most critical position of the 12-volt electrical system by approximately 0.6 V.