The highways of the world are growing more congested. People are inherently bad drivers from a safety and system reliability perspective. Self-driving cars are one solution to this problem, as automation can remove human error and react consistently to unexpected events. Automated vehicles have been touted as a potential solution to improving highway utilization and increasing the safety of people on the roads. Automated vehicles have proven to be capable of interacting safely with human drivers, but the technology is still new. This means that there are points of failure that have not been discovered yet. The focus of this work is to provide a platform to evaluate the security and reliability of automated ground vehicles in an adversarial environment. An existing system was already in place, but it was limited to longitudinal control, relying on a steel cable to keep the vehicle on track. The upgraded platform was developed with computer vision to drive the vehicle around a track in order to facilitate an extended attack. Sensing and control methods for the platform are proposed to provide a baseline for the experimental platform. Vehicle control depends on extensive sensor systems to determine the vehicle position relative to its surroundings. A potential attack on a vehicle could be performed by jamming the sensors necessary to reliably control the vehicle. A method to extend the sensing utility of a camera is proposed as a countermeasure against a sensor jamming attack. A monocular camera can be used to determine the bearing to a target, and this work extends the sensor capabilities to estimate the distance to the target. This provides a redundant sensor if the standard distance sensor of a vehicle is compromised by a malicious agent. For a 320×200 pixel camera, the distance estimation is accurate between 0.5 and 3 m. One previously discovered vulnerability of automated highway systems is that vehicles can coordinate an attack to induce traffic jams and collisions. The effects of this attack on a vehicle system with mixed human and automated vehicles are analyzed. The insertion of human drivers into the system stabilizes the traffic jam at the cost of highway utilization.

Thesis (MScEng)--Stellenbosch University, 2012 / ENGLISH ABSTRACT: This thesis presents the design and implementation of an autonomous navigational system and the automation of a practical demonstrator vehicle. It validates the proposed navigation architecture using simple functional navigational modules on the said vehicle. The proposed navigation architecture is a hierarchical structure, with a mission planner at the top, followed by the route planner, the path planner and a vehicle controller with the vehicle hardware at the base. A vehicle state estimator and mapping module runs in parallel to provide feedback data. The controls of an all terrain vehicle are electrically actuated and equipped with feedback sensors to form a complete drive-by-wire solution. A steering controller and velocity control state machine are designed and implemented on an existing on-board controller that includes a six degrees-of-freedom kinematic state estimator. A lidar scanner detects obstacles. The lidar data is mapped in real time to a local three-dimensional occupancy grid using a Bayesian update process. Each lidar beam is projected within the occupancy grid and the occupancy state of a ected cells is updated. A lidar simulation environment is created to test the mapping module before practical implementation. For planning purposes, the three-dimensional occupancy grid is converted to a two-dimensional drivability map. The path planner is an adapted rapidly exploring random tree (RRT) planner, that assumes Dubins car kinematics for the vehicle. The path planner optimises a cost function based on path length and a risk factor that is derived from the drivability map. A simple mission planner that accepts user-de ned waypoints as objectives is implemented. Practical tests veri ed the potential of the navigational structure implemented in this thesis. / AFRIKAANSE OPSOMMING: In hierdie tesis word die ontwerp en implementering van 'n outonome navigasiestelsel weergegee, asook die outomatisering van 'n praktiese demonstrasievoertuig. Dit regverdig die voorgestelde navigasie-argitektuur op die bogenoemde voertuig deur gebruik te maak van eenvoudige, funksionele navigasie-modules. Die voorgestelde navigasie-argitektuur is 'n hi erargiese struktuur, met die missie-beplanner aan die bo-punt, gevolg deur die roetebeplanner, die padbeplanner en voertuigbeheerder, met die voertuighardeware as basisvlak. 'n Voertuigtoestandsafskatter en karteringsmodule loop in parallel om terugvoer te voorsien. Die kontroles van 'n vierwiel-motor ets is elektries geaktueer en met terugvoersensors toegerus om volledig rekenaarbeheerd te wees. 'n Stuur-beheerder en 'n snelheid-toestandmasjien is ontwerp en ge mplementeer op 'n bestaande aanboordverwerker wat 'n kinematiese toestandsafskatter in ses grade van vryheid insluit. 'n Lidar-skandeerder registreer hindernisse. Die lidar-data word in re ele tyd na 'n lokale drie-dimensionele besettingsrooster geprojekteer deur middel van 'n Bayesiese opdateringsproses. Elke lidar-straal word in die besettingsrooster geprojekteer en die besettingstoestand van betrokke selle word opdateer. 'n Lidar-simulasie-omgewing is geskep om die karteringsmodule te toets voor dit ge mplementeer word. Die drie-dimensionele besettingsrooster word na 'n twee-dimensionele rybaarheidskaart verwerk vir beplanningsdoeleindes. Die padbeplanner is 'n aangepaste spoedig-ontdekkende-lukrake-boom en neem Dubinskar kinematika vir die voertuig aan. Die padbeplanner optimeer 'n koste-funksie, gebaseer op padlengte en 'n risiko-faktor, wat vanaf die rybaarheidskaart verkry word. 'n Eenvoudige missie-beplanner, wat via-punte as doelstellings neem, is ge mplementeer. Praktiese toetsritte veri eer die potensiaal van die navigasiestruktuur, soos hier beskryf.

Advanced Driver Assistance Systems (ADAS) and semi-autonomous driving systems are intended to enhance driver performance and improve transportation safety. The potential benefits of these technologies, such as reduction in number of crashes, enhancing driver comfort or convenience, decreasing environmental impact, etc., are well accepted and endorsed by transportation safety researchers and federal transportation agencies. Even though these systems afford safety advantages, they challenge the traditional role of drivers in operating vehicles. Driver acceptance, therefore, is essential for the implementation of ADAS and semi-autonomous driving systems into the transportation system. These technologies will not achieve their potential if drivers do not accept them and use them in a sustainable and appropriate manner. The potential benefits of these in-vehicle assistive systems presents a strong need for research. A comprehensive review of current literature on the definitions of acceptance, acceptance modelling approaches, and assessment techniques was carried out to explore and summarize the different approaches adopted by previous researchers. The review identified three major research needs: a comprehensive evaluation of general technology acceptance models in the context of ADAS, development of an acceptance model specifically for ADAS and similar technologies, and development of an acceptance assessment questionnaire. Two studies were conducted to address these needs. In the first study, data collection was done using two approaches: a driving simulator approach and an online survey approach. In both approaches, participants were exposed to an ADAS and, based on their experience, responded to several survey questions to indicate their attitude toward using the ADAS and their perception of its usefulness, usability, reliability, etc. The results of the first study showed the utility of the general technology acceptance theories to model driver acceptance. A Unified Model of Driver Acceptance (UMDA) and two versions (a long version with 21 items and a short version with 13 items) of an acceptance assessment questionnaire were also developed, based on the results of the first study. The second was conducted to validate the findings of first study. The results of the second study found statistical evidence validating UMDA and the two versions of the acceptance assessment questionnaire.

A prominent issue in the field of automotive research is the apparent lack of consideration given to the potentially safety-critical differences between novice and experienced users of Advanced Driver Assistance System (ADAS). Conducting experiments with novices only often results in the generation of unrepresentative findings, as these new systems often come with a lengthy adaptation period following their introduction. Running experiments with experienced drivers, however, is difficult, as these are often few and far between, if they even exist. To alleviate this discrepancy, and to help researchers acquire participants more akin to experienced drivers, even before a system has been launched, the approach of AcceLerated Behavioural Adaptation through Targeted tRaining prOgramS (ALBATROS) is proposed. It aims at training drivers in the use of the system, ideally giving them a level of experience similar to experienced users of said systems. A framework for the ALBATROS approach is presented, as is the development of a proof-of-concept training program following this approach. Likewise, a mock-up ADAS, that provides drivers with both longitudinal and lateral support of the vehicle, dubbed the Driver Assist (DA), is presented, for which the training program (the DATP) is developed. The current study presents an experiment designed to validate the efficacy of the DATP, and ultimately, the ALBATROS approach itself. The current study concludes that DATP-trained drivers display significantly improved understanding of the DA system following training and are significantly more likely to retake control in critical situations, than are untrained drivers. Thus, the ALBATROS approach appears a viable approach in giving drivers a better understanding of an ADAS system. However, whether the DATP succeeded in creating drivers similar in experience and understanding to real experienced users of said sys- tem, and if so, exactly how similar, is still unknown. More research is needed, specifically, studies comparing experienced users with those having been trained with the ALBATROS approach.

<div>Vehicle automation is developing at a rapid rate worldwide. However, even lower levels of automation, such as SAE Level-1, are expected to reduce drivers’ workload by controlling either speed or lane position. At the same time, however, drivers’ engagement in secondary tasks may make up for this difference in workload displaced by automation. Previous research has investigated the effects of adaptive cruise control (ACC) on driving performance and workload, but little attention has been devoted to Lane Keeping Systems (LKS). In addition, the influence of secondary cognitive tasks on Level-1 driving performance is also not well understood.</div><div><br></div><div>The first goal of this thesis study was to examine the effects of secondary cognitive tasks and driving condition on driving performance. The second goal was to examine the effects of secondary cognitive tasks and driving condition on heart rate related measurements that reflect changes in workload. Both a novel nano-sensor and a commercial ECG sensor were used to measure heart rate. Thus, the third goal was to compare the capability of a nano-sensor in detecting changes in heart rate and heart rate variability with a commercially available ECG sensor. Twenty-five participants drove a simulated vehicle in manual, ACC and LKS driving conditions, while performing a secondary cognitive (N-back) task with varying levels of difficulty.</div><div><br></div><div>Results showed that more difficult cognitive secondary tasks were beneficial to driving performance in that a lower standard deviation of lane departure (SDLD) and a lower standard deviation of vehicle speed (SDVS) were both observed. Heart rate and NASA-TLX workload scores were significantly higher in the most difficult secondary task and in the manual driving conditions. However, heart rate variability measures (SDNN, RMSSD, pNN50, LF Power and HF Power) indicated lower variability under more difficult secondary tasks. This thesis suggests that nanotechnological devices may serve as a potential alternative to other heart rate measuring technology. Limitations in detecting minor heart rate changes between different driving conditions and in heart rate variability measuring were also acknowledged.</div>