Statistical modelling of algorithms for signal processing in systems based on environment perception

One cornerstone for realising automated driving systems is an appropriate handling of uncertainties in the environment perception and situation interpretation. Uncertainties arise due to noisy sensor measurements or the unknown future evolution of a traffic situation. This work contributes to the understanding of these uncertainties by modelling and propagating them with parametric probability distributions

Intelligent automatic overtaking system using vision for vehicle detection

There is clear evidence that investment in intelligent transportation system technologies brings major social and economic benefits. Technological advances in the area of automatic systems in particular are becoming vital for the reduction of road deaths. We here describe our approach to automation of one the riskiest autonomous manœuvres involving vehicles – overtaking. The approach is based on a stereo vision system responsible for detecting any preceding vehicle and triggering the autonomous overtaking manœuvre. To this end, a fuzzy-logic based controller was developed to emulate how humans overtake. Its input is information from the vision system and from a positioning-based system consisting of a differential global positioning system (DGPS) and an inertial measurement unit (IMU). Its output is the generation of action on the vehicle’s actuators, i.e., the steering wheel and throttle and brake pedals. The system has been incorporated into a commercial Citroën car and tested on the private driving circuit at the facilities of our research center, CAR, with different preceding vehicles – a motorbike, car, and truck – with encouraging results

Clothoid-based Planning and Control in Intelligent Vehicles (Autonomous and Manual-Assisted Driving)

[EN] Nowadays, there are many electronic products that incorporate elements and features coming from the research in the field of mobile robotics. For instance, the well-known vacuum cleaning robot Roomba by iRobot, which belongs to the field of service robotics, one of the most active within the sector. There are also numerous autonomous robotic systems in industrial warehouses and plants. It is the case of Autonomous Guided Vehicles (AGVs), which are able to drive completely autonomously in very structured environments. Apart from industry and consumer electronics, within the automotive field there are some devices that give intelligence to the vehicle, derived in most cases from advances in mobile robotics. In fact, more and more often vehicles incorporate Advanced Driver Assistance Systems (ADAS), such as navigation control with automatic speed regulation, lane change and overtaking assistant, automatic parking or collision warning, among other features. However, despite all the advances there are some problems that remain unresolved and can be improved. Collisions and rollovers stand out among the most common accidents of vehicles with manual or autonomous driving. In fact, it is almost impossible to guarantee driving without accidents in unstructured environments where vehicles share the space with other moving agents, such as other vehicles and pedestrians. That is why searching for techniques to improve safety in intelligent vehicles, either autonomous or manual-assisted driving, is still a trending topic within the robotics community. This thesis focuses on the design of tools and techniques for planning and control of intelligent vehicles in order to improve safety and comfort. The dissertation is divided into two parts, the first one on autonomous driving and the second one on manual-assisted driving. The main link between them is the use of clothoids as mathematical formulation for both trajectory generation and collision detection. Among the problems solved the following stand out: obstacle avoidance, rollover avoidance and advanced driver assistance to avoid collisions with pedestrians.[ES] En la actualidad se comercializan infinidad de productos de electrónica de consumo que incorporan elementos y características procedentes de avances en el sector de la robótica móvil. Por ejemplo, el conocido robot aspirador Roomba de la empresa iRobot, el cual pertenece al campo de la robótica de servicio, uno de los más activos en el sector. También hay numerosos sistemas robóticos autónomos en almacenes y plantas industriales. Es el caso de los vehículos autoguiados (AGVs), capaces de conducir de forma totalmente autónoma en entornos muy estructurados. Además de en la industria y en electrónica de consumo, dentro del campo de la automoción también existen dispositivos que dotan de cierta inteligencia al vehículo, derivados la mayoría de las veces de avances en robótica móvil. De hecho, cada vez con mayor frecuencia los vehículos incorporan sistemas avanzados de asistencia al conductor (ADAS por sus siglas en inglés), tales como control de navegación con regulación automática de velocidad, asistente de cambio de carril y adelantamiento, aparcamiento automático o aviso de colisión, entre otras prestaciones. No obstante, pese a todos los avances siguen existiendo problemas sin resolver y que pueden mejorarse. La colisión y el vuelco destacan entre los accidentes más comunes en vehículos con conducción tanto manual como autónoma. De hecho, la dificultad de conducir en entornos desestructurados compartiendo el espacio con otros agentes móviles, tales como coches o personas, hace casi imposible garantizar la conducción sin accidentes. Es por ello que la búsqueda de técnicas para mejorar la seguridad en vehículos inteligentes, ya sean de conducción autónoma o manual asistida, es un tema que siempre está en auge en la comunidad robótica. La presente tesis se centra en el diseño de herramientas y técnicas de planificación y control de vehículos inteligentes, para la mejora de la seguridad y el confort. La disertación se ha dividido en dos partes, la primera sobre conducción autónoma y la segunda sobre conducción manual asistida. El principal nexo de unión es el uso de clotoides como elemento de generación de trayectorias y detección de colisiones. Entre los problemas que se resuelven destacan la evitación de obstáculos, la evitación de vuelcos y la asistencia avanzada al conductor para evitar colisiones con peatones.[CA] En l'actualitat es comercialitzen infinitat de productes d'electrònica de consum que incorporen elements i característiques procedents d'avanços en el sector de la robòtica mòbil. Per exemple, el conegut robot aspirador Roomba de l'empresa iRobot, el qual pertany al camp de la robòtica de servici, un dels més actius en el sector. També hi ha nombrosos sistemes robòtics autònoms en magatzems i plantes industrials. És el cas dels vehicles autoguiats (AGVs), els quals són capaços de conduir de forma totalment autònoma en entorns molt estructurats. A més de en la indústria i en l'electrònica de consum, dins el camp de l'automoció també existeixen dispositius que doten al vehicle de certa intel·ligència, la majoria de les vegades derivats d'avanços en robòtica mòbil. De fet, cada vegada amb més freqüència els vehicles incorporen sistemes avançats d'assistència al conductor (ADAS per les sigles en anglés), com ara control de navegació amb regulació automàtica de velocitat, assistent de canvi de carril i avançament, aparcament automàtic o avís de col·lisió, entre altres prestacions. No obstant això, malgrat tots els avanços segueixen existint problemes sense resoldre i que poden millorar-se. La col·lisió i la bolcada destaquen entre els accidents més comuns en vehicles amb conducció tant manual com autònoma. De fet, la dificultat de conduir en entorns desestructurats compartint l'espai amb altres agents mòbils, tals com cotxes o persones, fa quasi impossible garantitzar la conducció sense accidents. És per això que la recerca de tècniques per millorar la seguretat en vehicles intel·ligents, ja siguen de conducció autònoma o manual assistida, és un tema que sempre està en auge a la comunitat robòtica. La present tesi es centra en el disseny d'eines i tècniques de planificació i control de vehicles intel·ligents, per a la millora de la seguretat i el confort. La dissertació s'ha dividit en dues parts, la primera sobre conducció autònoma i la segona sobre conducció manual assistida. El principal nexe d'unió és l'ús de clotoides com a element de generació de trajectòries i detecció de col·lisions. Entre els problemes que es resolen destaquen l'evitació d'obstacles, l'evitació de bolcades i l'assistència avançada al conductor per evitar col·lisions amb vianants.Girbés Juan, V. (2016). Clothoid-based Planning and Control in Intelligent Vehicles (Autonomous and Manual-Assisted Driving) [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/65072TESI

Safety-critical scenarios and virtual testing procedures for automated cars at road intersections

This thesis addresses the problem of road intersection safety with regard to a mixed population of automated vehicles and non-automated road users. The work derives and evaluates safety-critical scenarios at road junctions, which can pose a particular safety problem involving automated cars. A simulation and evaluation framework for car-to-car accidents is presented and demonstrated, which allows examining the safety performance of automated driving systems within those scenarios. Given the recent advancements in automated driving functions, one of the main challenges is safe and efficient operation in complex traffic situations such as road junctions. There is a need for comprehensive testing, either in virtual testing environments or on real-world test tracks. Since it is unrealistic to cover all possible combinations of traffic situations and environment conditions, the challenge is to find the key driving situations to be evaluated at junctions. Against this background, a novel method to derive critical pre-crash scenarios from historical car accident data is presented. It employs k-medoids to cluster historical junction crash data into distinct partitions and then applies the association rules algorithm to each cluster to specify the driving scenarios in more detail. The dataset used consists of 1,056 junction crashes in the UK, which were exported from the in-depth On-the-Spot database. The study resulted in thirteen crash clusters for T-junctions, and six crash clusters for crossroads. Association rules revealed common crash characteristics, which were the basis for the scenario descriptions. As a follow-up to the scenario generation, the thesis further presents a novel, modular framework to transfer the derived collision scenarios to a sub-microscopic traffic simulation environment. The software CarMaker is used with MATLAB/Simulink to simulate realistic models of vehicles, sensors and road environments and is combined with an advanced Monte Carlo method to obtain a representative set of parameter combinations. The analysis of different safety performance indicators computed from the simulation outputs reveals collision and near-miss probabilities for selected scenarios. The usefulness and applicability of the simulation and evaluation framework is demonstrated for a selected junction scenario, where the safety performance of different in-vehicle collision avoidance systems is studied. The results show that the number of collisions and conflicts were reduced to a tenth when adding a crossing and turning assistant to a basic forward collision avoidance system. Due to its modular architecture, the presented framework can be adapted to the individual needs of future users and may be enhanced with customised simulation models. Ultimately, the thesis leads to more efficient workflows when virtually testing automated driving at intersections, as a complement to field operational tests on public roads

Trends in vehicle motion control for automated driving on public roads

In this paper, we describe how vehicle systems and the vehicle motion control are affected by automated driving on public roads. We describe the redundancy needed for a road vehicle to meet certain safety goals. The concept of system safety as well as system solutions to fault tolerant actuation of steering and braking and the associated fault tolerant power supply is described. Notably restriction of the operational domain in case of reduced capability of the driving automation system is discussed. Further we consider path tracking, state estimation of vehicle motion control required for automated driving as well as an example of a minimum risk manoeuver and redundant steering by means of differential braking. The steering by differential braking could offer heterogeneous or dissimilar redundancy that complements the redundancy of described fault tolerant steering systems for driving automation equipped vehicles. Finally, the important topic of verification of driving automation systems is addressed

Naturalistic Driver Intention and Path Prediction using Machine Learning

Autonomous vehicles are still yet to be available to the public. This is because there are a number of challenges that have not been overcome to ensure that autonomous vehicles can safely and efficiently drive on public roads. Accurate prediction of other vehicles is vital for safe driving, as interacting with other vehicles is unavoidable on public streets. This thesis explores reasons why this problem of scene understanding is still unsolved, and presents methods for driver intention and path prediction. The thesis focuses on intersections, as this is a very complex scenario in which to predict the actions of human drivers. There is very limited data available for intersection studies from the perspective of an autonomous vehicle. This thesis presents a very large dataset of over 23,000 vehicle trajectories, used to validate the algorithms presented in this thesis. This dataset was collected using a lidar based vehicle detection and tracking system onboard a vehicle. Analytics of this data is presented. To determine the intent of vehicle at an intersection, a method for manoeuvre classification through the use of recurrent neural networks is presented. This allows accurate predictions of which destination a vehicle will take at an unsignalised intersection, based on that vehicle's approach. The final contribution of this thesis presents a method for driver path prediction, based on recurrent neural networks. It produces a multi-modal prediction for the vehicle’s path with uncertainty assigned to each mode. The output modes are not hand labelled, but instead learned from the data. This results in there not being a fixed number of output modes. Whilst the application of this method is vehicle prediction, this method shows significant promise to be used in other areas of robotics

Isesõitvate autode tee planeerimine baseerudes inimese tegevuse tuvastamisele

Human activity recognition (HAR) is wide research topic in a field of computer science. Improving HAR can lead to massive breakthrough in humanoid robotics, robots used in medicine and in the field of autonomous vehicles. The system that is able to recognise human and its activity without any errors and anomalies, would lead to safer and more empathetic autonomous systems. During this thesis multiple neural networks models, with different complexity, are being investigated. Each model is re-trained on the proposed unique data set, gathered on automated guided vehicle (AGV) with the latest and the modest sensors used commonly on autonomous vehicles. The best model is picked out based on the final accuracy for action recognition. Best models pipeline is fused with YOLOv3, to enhance the human detection. In addition to pipeline improvement, multiple action direction estimation methods are proposed. The action estimation of the human is very important aspect for self-driving car collision free path planning