AutonoVi: Autonomous Vehicle Planning with Dynamic Maneuvers and Traffic Constraints

We present AutonoVi:, a novel algorithm for autonomous vehicle navigation that supports dynamic maneuvers and satisfies traffic constraints and norms. Our approach is based on optimization-based maneuver planning that supports dynamic lane-changes, swerving, and braking in all traffic scenarios and guides the vehicle to its goal position. We take into account various traffic constraints, including collision avoidance with other vehicles, pedestrians, and cyclists using control velocity obstacles. We use a data-driven approach to model the vehicle dynamics for control and collision avoidance. Furthermore, our trajectory computation algorithm takes into account traffic rules and behaviors, such as stopping at intersections and stoplights, based on an arc-spline representation. We have evaluated our algorithm in a simulated environment and tested its interactive performance in urban and highway driving scenarios with tens of vehicles, pedestrians, and cyclists. These scenarios include jaywalking pedestrians, sudden stops from high speeds, safely passing cyclists, a vehicle suddenly swerving into the roadway, and high-density traffic where the vehicle must change lanes to progress more effectively.Comment: 9 pages, 6 figure

Low resolution lidar-based multi object tracking for driving applications

The final publication is available at link.springer.comVehicle detection and tracking in real scenarios are key com- ponents to develop assisted and autonomous driving systems. Lidar sen- sors are specially suitable for this task, as they bring robustness to harsh weather conditions while providing accurate spatial information. How- ever, the resolution provided by point cloud data is very scarce in com- parison to camera images. In this work we explore the possibilities of Deep Learning (DL) methodologies applied to low resolution 3D lidar sensors such as the Velodyne VLP-16 (PUCK), in the context of vehicle detection and tracking. For this purpose we developed a lidar-based sys- tem that uses a Convolutional Neural Network (CNN), to perform point- wise vehicle detection using PUCK data, and Multi-Hypothesis Extended Kalman Filters (MH-EKF), to estimate the actual position and veloci- ties of the detected vehicles. Comparative studies between the proposed lower resolution (VLP-16) tracking system and a high-end system, using Velodyne HDL-64, were carried out on the Kitti Tracking Benchmark dataset. Moreover, to analyze the influence of the CNN-based vehicle detection approach, comparisons were also performed with respect to the geometric-only detector. The results demonstrate that the proposed low resolution Deep Learning architecture is able to successfully accom- plish the vehicle detection task, outperforming the geometric baseline approach. Moreover, it has been observed that our system achieves a similar tracking performance to the high-end HDL-64 sensor at close range. On the other hand, at long range, detection is limited to half the distance of the higher-end sensor.Peer ReviewedPostprint (author's final draft

Multi-attribute decision making on mitigating a collision of an autonomous vehicle on motorways

Autonomous vehicles have the potential to improve automotive safety, largely by removing human error as a possible cause of collisions. However, it cannot be guaranteed that autonomous vehicles will be able to eliminate all collisions. Therefore, automotive safety will continue to be a necessity for automotive design. This paper proposes a decision making system which selects the least severe collision for an autonomous vehicle to take, when facing multiple imminent and unavoidable collisions on a motorway. The novel decision making system developed combines simulation results and multi-attribute decision making (MADM) methods. The simulator includes models of vehicle dynamics and the manoeuvre trajectory path. MADM methods are used to decide which vehicle(s) the autonomous vehicle should collide with, based on the severity of collisions. Severity of collisions is calculated in the simulator using the following variables: impact velocity between autonomous vehicle and vehicle ahead, impact velocity between vehicle behind and autonomous vehicle, manoeuvre acceleration and time-to-collision. Various MADM methods are investigated and three methods are selected including the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the Analytical Hierarchy Process (AHP), and the Analytical Network Process (ANP). Various collision scenarios are defined and tested in order to understand the impact that small changes in parameters of the autonomous vehicle and vehicles ahead and behind have on the decision made. The analysed decision making results are promising and lead to the conclusion that MADM methods can be successfully applied in autonomous vehicles

Collision Avoidance via Adaptive Trajectory Control in Case of a Sudden Decrease in the Maximum Road Friction Coefficient

This study analyses an Adaptive Trajectory Control (ATC) system in case of a sudden change in μ-max (maximum tyreroad friction coefficient) during an emergency lane change manoeuvre in order to maintain the driving safety. Autonomous front wheel steering (FWS) systems have been developed for emergency steering situations. The trajectory design is also a part of these systems. Moreover, in this study ATC has been designed by sensing μ-max to complete the emergency steering manoeuvre successfully. Therefore, the originality of this paper arises from the necessity of a trajectory change in case of a sudden change in μ-max to minimize the distance between the desired and the actual path. Suitable cases were designed by using a two-track model in IPG/CarMaker (MATLAB/Simulink). Results show that ATC could be used during an emergency steering manoeuvre in case of a sudden decrease in μ-max as it can be advantageous in certain critical traffic situations. Therefore, ATC could beused as an alternative system instead of Electronic Stability Program.</p

Influence of wireless communication transport latencies and dropped packages on vehicle stability with an offsite steering controller

In recent years, advanced driver assistance systems (ADASs) have been used to improve the safety of vehicles by either providing additional information to the driver or by taking over complete control. The majority of ADASs currently being utilised run entirely on the vehicle, only having access to information provided by the sensors that are onboard the vehicle itself. Part of the next step in the evolution of ADAS is to incorporate information from other offsite sensors or obtain control inputs from infrastructure which can coordinate multiple vehicles simultaneously via a wireless interface. Wireless communication is inherently delayed and prone to dropped packets. This study looks at the effect of transport latencies and dropped packets on an off-site autoregressive steering controller supplying direct steering inputs to a vehicle. A fully non-linear vehicle simulation model is used to test the effect of delaying steering inputs and dropped packets to test the stability of the controller. The study shows that at dropped packet percentages of up to 40% adequate vehicle control is maintained, while transport latencies of up to 100 ms allow for moderately accurate vehicle control.http://www.ietdl.org/IET-ITShj2020Mechanical and Aeronautical Engineerin

APPLICATION OF PARAMETER ESTIMATION AND CALIBRATION METHOD FOR CAR-FOLLOWING MODELS

Both safety and the capacity of the roadway system are highly dependent on the car-following characteristics of drivers. Car-following theory describes the driver behavior of vehicles following other vehicles in a traffic stream. In the last few decades, many car-following models have been developed; however, studies are still needed to improve their accuracy and reliability. Car-following models are a vital component of traffic simulation tools that attempt to mimic driver behavior in the real world. Microscopic traffic simulators, particularly car-following models, have been extensively used in current traffic engineering studies and safety research. These models are a vital component of traffic simulation tools that attempt to mimic real-world driver behaviors. The accuracy and reliability of microscopic traffic simulation models are greatly dependent on the calibration of car-following models, which requires a large amount of real world vehicle trajectory data. In this study, the author developed a process to apply a stochastic calibration method with appropriate regularization to estimate the distribution of parameters for car-following models. The calibration method is based on the Markov Chain Monte Carlo (MCMC) simulation using the Bayesian estimation theory that has been recently investigated for use in inverse problems. This dissertation research includes a case study, which is based on the Linear (Helly) model with a different number of vehicle trajectories in a highway network. The stochastic approach facilitated the calibration of car-following models more realistically than the deterministic method, as the deterministic algorithm can easily get stuck at a local minimum. This study also demonstrates that the calibrated model yields smaller errors with large sample sizes. Furthermore, the results from the Linear model validation effort suggest that the performance of the calibration method is dependent upon size of the vehicle trajectory

Analyse et développement de radar à diversité spatiale: applications à l'évitement de collisions de véhicules et au positionnement local

RÉSUMÉ En tant que dispositif d’assistance à la conduite sécuritaire de la prochaine génération d’automobiles, les radars ont suscité beaucoup d’intérêt auprès des chercheurs du domaine au cours de la dernière décennie. Désormais appelés les radars à évitement/avertissement des collisions (collision avoidance/warning), ces radars ouvrent leurs chemins pour venir en aide aux conducteurs dans les conditions climatiques difficiles ou en perte de concentration. Une autre application récente des radars est dans les systèmes de positionnement local. Dans les milieux industriels et médicaux, nous avons besoin de localiser les équipements sollicités fréquemment dont les contraintes de coût et d’encombrement limitent le nombre d’exemplaires. Par ailleurs, avec le vieillissement de la population et les besoins croissants des personnes âgées en soins médicaux, la nécessité d’un système permettant l’évaluation à distance de la position (debout, couché, tombé, …) des patients se fait sentir depuis un certain temps. Un autre exemple est la surveillance des enfants et des personnes à risque dans un endroit peuplé comme dans une foire ou sur une plage. La possibilité de pouvoir repérer les pompiers dans un immeuble en feu serait une autre application intéressante. Devant la multitude de ces applications potentielles et attrayantes dont les contextes évoluent, le système de positionnement local doit à son tour évoluer et s’adapter. Par ailleurs, il est bien connu que les radars, comme tous les systèmes de télécommunications sans fil, sont confrontés au problème d’évanouissement du signal. D’une manière générale, ce problème est dû aux propagations multi-chemins du signal. Autrement dit, les réflexions multiples du signal par les objets environnants mobiles et stationnaires se neutralisent de façon aléatoire au point d’arrivée où se trouve l’antenne réceptrice. Dans un contexte différent et pour des raisons à priori différentes, les radars subissent le même type de défaillances. Même dans un milieu dégagé et avec la visibilité directe (line of sight) sur la cible, les radars sont exposés au problème d’évanouissement du signal (power fading) dû aux changements de la surface équivalente radar (radar cross section) de la cible.----------ABSTRACT As a device enabling the safe driving of the next generation of vehicles, the radars have trigged much interest among the researchers of this field in the last decade. Recently called collision avoidance/warning radar, this type of radar can assist drivers in bad weather conditions and when driver’s concentration and attention fails. In the other hand, the utilization context of systems has evolved and will go even further in the upcoming years. In the industrial locations and medical centers, we need to locate most requested equipments. With aging society and the growing needs of elder people for medical care, a system capable of remotely sensing the patients (standing, lying down or falling) has been studied since the beginning of the new century. Other interesting example would include the surveillance of children in crowded places (beaches or amusement parks) or locating fire fighters in a building. In a context of evolving applications, the vertical local positioning system should also evolve. The most frequently used method in local positioning systems is to make use of three base stations at different places and to measure the range of the tag by each base station. Then the exact location of the tag is calculated by triangulation. In practice, a fourth base station is added for more reliability and time synchronization. In some situations like the surveillance of a beach or a building on fire, installing the third base station would be a difficult or time consuming task. Our idea is to elevate the third base station at a reasonable height. This will provide a better signal quality and more information about the target can be obtained. It is a new type of local positioning system that we call VLPS (Vertical Local Positioning System). We will examine the constraints of VLPS in the second part of this thesis. Moreover, it is well known that the radars, as well as all wireless telecommunication systems, are confronted with the problem of fading signals. Generally, this problem is due to multi-path effects of signal propagations. In other words, the multiple signal reflections by the surrounding stationary and mobile objects are randomly neutralized at the arriving point of the receiving antenna. In a different context and for apparently unlike motives, the radars are subject to the same issue. Even when the target is in the line-up site of transmitting and receiving antennas (radars), they face the same type of scintillations due to the variation of the radar cross section (RCS) of a target. Indeed, the radar cross section of the majority of targets strongly depends on the aspect angles of the receiving and transmitting antennas

Potenzial eines Griffkraft messenden Lenkrades als Sensor innerhalb sicherheitsorientierter Fahrerassistenzsysteme

Die hier vorgelegte Arbeit hatte zum Ziel, die Verwendung haptischer Fahrereingaben zur Detektion von Gefahrensituationen in Ihrem Spektrum zu erweitern. Es sollte untersucht werden, ob kritische Situationen im Straßenverkehr mit einer Erhöhung der am Lenkrad aufgebrachten Griffkraft einhergehen, um diese Information mit bestehenden Messgrößen zu fusionieren und zu einer verbesserten Klassifikation von Notsituation zu verwenden