With autonomy becoming a reality for passenger cars, developing an autonomous for tractor-trailers is the next step for driverless roads. Tractor-trailers are heavy, large, and have a pivot joint between the tractor and trailer that makes the movement between the two more complicated. The purpose of the research presented here is to determine the best forward "looking" perception sensor that will see far out enough for the vehicle to stop in time to avoid hitting an object. In order to determine the best sensor, a review of previous sensors and autonomous vehicle sensors will be explored along with the various perception technology. Additionally, a simulation of a tractor-trailer stopping was created to determine the range necessary for a forward perception sensor and when jackknifing may occur. The best brake type for a tractor-trailer will be recommended as well. Finally, the best forward sensor and senor layout for an autonomous tractor-trailer is made based upon the simulation results for the stopping distance of a tractor-trailer. The work, however, is not fully complete. A discussion of the future work and validation of the sensors selected will give future research goals. / Master of Science / With autonomy becoming a reality for passenger cars, developing an autonomous for tractor-trailers is the next step for driverless roads. Tractor-trailers are heavy, large, and have a pivot joint between the tractor and trailer that makes the movement between the two more complicated. The purpose of the research presented here is to determine the best forward “looking” perception sensor that will see far out enough for the vehicle to stop in time to avoid hitting an object. In order to determine the best sensor, a review of previous sensors and autonomous vehicle sensors will be explored along with the various perception technology. Additionally, a simulation of a tractor-trailer stopping was created to determine the range necessary for a forward perception sensor and when jackknifing may occur. The best brake type for a tractor-trailer will be recommended as well. Finally, the best forward sensor and senor layout for an autonomous tractor trailer is made based upon the simulation results for the stopping distance of a tractor-trailer. The work, however, is not fully complete. A discussion of the future work and validation of the sensors selected will give future research goals.

This study addresses the application and the effectiveness of radar obstacle sensors for forklift trucks during reverse travel. Two different discriminating radar obstacle sensors with different outputs are evaluated. This study reviews the safety of human exposure to emissions from these radar sensors; documents the field of view obtained from experiments with the two systems; gives the results from experiments with sensors on lift trucks. The influence of obstacle reflectivity, composition and area on the size and shape of the radar detection zone are discussed. An experimental setup for measuring position and velocity of the obstacle crossing the truck path is described. The combination of obstacle sensors required for full coverage of the back of the lift trucks and the mounting height and angle are discussed.

This diploma thesis is engaged in analysis of braking process of vehicles, especially heavy utility vehicles, with regard to their instantaneous weight. This thesis features survey and division of braking systems and function of brakes including schemes and descriptions. Also there is introduce of some systems improving vehicle properties. Furthermore this work puts mind to possibilities of examination of brakes, measurement of braking distance and braking retardation namely in brake test rooms or by outdoor driving tests including descriptions of particular methods and equipment. There is also mention about legislative requirements for braking distance and braking retardation. The conclusion of this thesis compares numerically predicted braking distance and real braking distance measured for factual vehicle during outdoor driving test

Trafiksäkerhet är något som är viktigt för trafikanten och den som arbetar på vägarna. Säkerheten längs vägar är något som har utvecklats, men inte flyttbara säkerhetsbarriär för vägarbeten. Den här rapporten följer ett produktutvecklingsprojekt där ett koncept för en ny trafikbuffert tas fram. Dagens trafikbuffertar är svåra att hantera, inte anpassade för stadsmiljöer och ger en lång stoppsträcka för fordonet vid kollision. Den ger även en hög kraftpåkänning för de inblandade vid kollision då det blir en hög impuls. Utgångspunkten för projektet har varit att minska stoppsträckan och minska de inblandas upplevda kraftpåkänning. Detta genomfördes genom att följa en beprövad produktutvecklingsprocess. Under förstudien upptäcktes att det fanns begränsat med information och forskning inom området, men det finns liknande lösningar som det togs inspiration från. Det togs sedan fram ett koncept som baserades på att använda en del av bilens vikt för att minska stoppsträckan, tillsammans med en deformationszon som ska minska rörelseenergin i bilen och kraftpåkänningen för de iblandade. Ett flertal iterationer gjordes på konceptet för att uppfylla så många krav som möjligt och bli ett realiserbart koncept. Simuleringar gjordes för att säkerställa hållfastheten på konstruktionen och beräkningar för att få en uppfattning om stoppsträckan.  Resultatet är ett koncept som har kortare stoppsträcka i teorin och en lägre kraftpåkänning än dagens använda trafikbuffert. Konceptet kräver vidare arbete för att definiera tillverkningsteknik och få ett godkännande av Trafikverket. Slutsatsen är att användningen av bilens vikt är ett bra sätt att minska stoppsträckan. Dock krävs krocksimuleringar och krocktester för att validera resultaten. / Traffic safety is something that is important for both drivers and road workers. Safety along roads has seen developments, but portable safety barriers for road work have not. This report follows a product development project where a concept for a new traffic buffer is being developed. Current traffic buffers are difficult to handle, not suitable for urban environments, and have a long stopping distance when a collision occurs. They also exert high force on those involved, resulting in a severe impact.  The goal of this project was to reduce the vehicle stopping distance and the perceived force on the passengers involved. A proven product development process was used to achieve this goal. During the preliminary study it was discovered that there was limited information and research in the field, but there are similar solutions that provided inspiration. A concept was then developed based on utilizing a portion of the vehicle's weight to reduce the stopping distance, along with a deformation zone that would reduce the vehicle's kinetic energy and the force on those involved. Several iterations were made on the concept to meet as many requirements as possible and make it a feasible concept. Simulations were conducted to ensure the structural strength and calculations were made to estimate the stopping distance. The result is a concept that requires further work to make it ready for production and approved by the Swedish Transport Administration. The concept has a theoretical stopping distance of 4.5 meters and lower force exertion. In conclusion, utilizing the vehicle's weight is a good way to reduce the stopping distance. However, crash simulations and tests are required to validate the results. / <p>Betygsdatum 2023-06-28</p>

Two standard graph representations for linear codes are the Tanner graph and the tailbiting trellis. Such graph representations allow the decoding problem for a code to be phrased as a computational problem on the corresponding graph and yield graph theoretic criteria for good codes. When a Tanner graph for a code is used for communication across a binary erasure channel (BEC) and decoding is performed using the standard iterative decoding algorithm, the maximum number of correctable erasures is determined by the stopping distance of the Tanner graph. Hence the computational problem of determining the stopping distance of a Tanner graph is of interest. In this thesis it is shown that computing stopping distance of a Tanner graph is NP hard. It is also shown that there can be no (1 + є ) approximation algorithm for the problem for any є > 0 unless P = NP and that approximation ratio of 2(log n)1- є for any є > 0 is impossible unless NPCDTIME(npoly(log n)). One way to construct Tanner graphs of large stopping distance is to ensure that the graph has large girth. It is known that stopping distance increases exponentially with the girth of the Tanner graph. A new elementary combinatorial construction algorithm for an almost regular LDPC code family with provable Ώ(log n) girth and O(n2) construction complexity is presented. The bound on the girth is close within a factor of two to the best known upper bound on girth. The problem of linear time exact maximum likelihood decoding of tailbiting trellis has remained open for several years. An O(n) complexity approximate maximum likelihood decoding algorithm for tail-biting trellises is presented and analyzed. Experiments indicate that the algorithm performs close to the ideal maximum likelihood decoder.

Annotation. This thesis presents some analysis of car braking process: a number of analythical formulas and graphic illustrations, discussion on different modes of braking, wider analysis of the main factors, having influence on the braking process. The main parameters and characteristic features, such as steady deceleration, driver’s reaction time, time of deceleration increase, restopping time and others, are also examined, values of most of them were specified during experimental research. A number of dependency graphs and numerical data, recommended for use in calculations performed during the examination of road accidents (and so improving the accuracy of the methods) was also presented.

This study presents a procedure for the estimation of stopping behavior of industrial robots with a trained neural network. This trained network is presented as a single channel in a redundant architecture for safety control applications, where its potential for future integration with an analytical model of robot stopping is discussed. Basic physical relations for simplified articulated manipulators are derived, which motivate a choice of quantities to predict robot stopping behavior and inform the training and testing of a network for prediction of stopping distances and times. Robot stopping behavior is considered in the context of relevant standards ISO 10218-1, ISO/TS 15066 and IS0 13849-1, which inform the definitions for safety related stopping distances and times used in this study. Prior work on the estimation of robot stopping behavior is discussed alongside applications of machine learning to the broader field of industrial robotics, and particularly to the cases of prediction of forward and inverse kinematics with trained networks. A state-driven data collection program is developed to perform repeated stopping experiments for a controlled stop on path within a specified sampling domain. This program is used to collect data for a simulated and real robot system. Special attention is given to the identification of meaningful stopping times, which includes the separation of stopping into pre-deceleration and post-deceleration phases. A definition is provided for stopping of a robot in a safety context, based on the observation that residual motion over short distances (less than 1 mm) and at very low velocities (less than 1 mm/s) is not relevant to robot safety. A network architecture and hyperparameters are developed for the prediction of stopping distances and times for the first three joints of the manipulator without the inclusion of payloads. The result is a dual-network structure, where stopping distance predictions from the distance prediction network serve as inputs to the stopping time prediction network. The networks are validated on their capacity to interpolate and extrapolate predictions of robot stopping behavior in the presence of initial conditions not included in the training and testing data. A method is devised for the calculation of prediction errors for training training, testing and validation data. This method is applied both to interpolation and extrapolation to new initial velocity and positional conditions of the manipulator. In prediction of stopping distances and times, the network is highly successful at interpolation, resulting in comparable or nominally higher errors for the validation data set when compared to the errors for training and testing data. In extrapolation to new initial velocity and positional conditions, notably higher errors in the validation data predictions are observed for the networks considered. Future work in the areas of predictions of stopping behavior with payloads and tooling, further applications to collaborative robotics, analytical models of stopping behavior, inclusion of additional stopping functions, use of explainable AI methods and physics-informed networks are discussed. / Master of Science / As the uses for industrial robots continue to grow and expand, so do the need for robust safety measures to avoid, control, or limit the risks posed to human operators and collaborators. This is exemplified by Isaac Asimov's famous first law of robotics - "A robot may not injure a human being, or, through inaction, allow a human being to come to harm." As applications for industrial robots continue to expand, it is beneficial for robots and human operators to collaborate in work environments without fences. In order to ethically implement such increasingly complex and collaborative industrial robotic systems, the ability to limit robot motion with safety functions in a predictable and reliable way (as outlined by international standards) is paramount. In the event of either a technical failure (due to malfunction of sensors or mechanical hardware) or change in environmental conditions, it is important to be able to stop an industrial robot from any position in a safe and controlled manner. This requires real-time knowledge of the stopping distance and time for the manipulator. To understand stopping distances and times reliability, multiple independent methods can be used and compared to predict stopping behavior. The use of machine learning methods is of particular interest in this context due to their speed of processing and the potential for basis on real recorded data. In this study, we will attempt to evaluate the efficacy of machine learning algorithms to predict stopping behavior and assess their potential for implementation alongside analytical models. A reliable, multi-method approach for estimating stopping distances and times could also enable further methods for safety in collaborative robotics such as Speed and Separation Monitoring (SSM), which monitors both human and robot positions to ensure that a safe stop is always possible. A program for testing and recording the stopping distances and times for the robot is developed. As stopping behavior varies based on the positions and speeds of the robot at the time of stopping, a variety of these criteria are tested with the robot stopping program. This data is then used to train an artificial neural network, a machine learning method that mimics the structure of human and animal brains to learn relationships between data inputs and outputs. This network is used to predict both the stopping distance and time of the robot. The network is shown to produce reasonable predictions, especially for positions and speeds that are intermediate to those used to train the network. Future improvements are suggested and a method is suggested for use of stopping distance and time quantities in robot safety applications.