Perceiving the surrounding multiple vehicles robustly and effectively is a very important step in building Advanced Driving Assistant System (ADAS) or autonomous vehicles. This thesis presents the design of the Light Detection and Ranging (LiDAR) perception system which consists of several sub-tasks: ground detection, object detection, object classification, and object tracking. It is believed that accomplishing these sub-tasks will provide a guideline to a vast range of potential autonomous vehicles applications. More specifically, a probability occupancy grid map based approach was developed for ground detection to address the issues of over-segmentation, under-segmentation and slow-segmentation by non-flat surface. Given the non-ground points, point cloud clustering algorithm is developed for object detection by using a Radially Bounded Nearest Neighbor (RBNN) method on the static Kd-tree. To identify the object, a supervised learning approach based on our LiDAR sensor for vehicle type classification is proposed. The proposed classification algorithm is used to classify the object into four different types: ``Sedan'', ``SUV'', ``Van'', and ``Truck''. To handle disturbances and motion uncertainties, a generalized form of Smooth Variable Structure Filter (SVSF) integrated with a combination of Hungarian algorithm (HA) and Probability Data Association Filter (PDAF), referred to as GSVSF-HA/PDAF, is developed. The developed approach is to overcome the multiple targets data association in the content of dynamics environment where the distribution of data is unpredictable. Last but not the least, a comprehensive experimental evaluation for each sub-task is presented to validate the robustness and effectiveness of our developed perception system. / Thesis / Doctor of Philosophy (PhD)

It is inevitable that as a person ages they will encounter different physical and cognitive impairments as well as dynamic social issues. We started this project under the assumption that autonomous driving would greatly benefit the fastest growing population in developed countries, the elderly. However, the larger question at hand was how are older drivers going to interact with future automated driving systems? It was through the qualitative research we conducted that we were able to uncover the answer to this question; older drivers are not willing to give up “control” to autonomous cars. As interaction designers, we need to define what type of interactions need to occur in these future automated driving systems, so older drivers still feel independent and in control when driving. Lawrence D. Burns, former Vice president of Research and Development at General Motors and author of Reinventing the Automobile Personal Urban Mobility for the 21st Century talks about two driving factors that will shape the future of the automobile. These factors are energy and connectivity (Burns et al., 2010). We would add a third one, which is control. If we address these three factors we might be able to bridge the gap between how we drive today and how we will drive in the future and thus create more cohesive future automated driving systems.

Autonomous Driving or self driving vehicles are concepts of vehicles knowing their environment and making driving manoeuvres without instructions from a driver. The concepts have been around for decades but has improved significantly in the last years since research in this area has made significant progress. Benefits of autonomous driving include the possibility to decrease the number of accidents in traffic and thereby saving lives. A major challenge in autonomous driving is to acquire 3D information and relations between all objects in surrounding traffic. This is referred to as \textit{spatial perception}. Stereo camera systems have become a central sensor module for advanced driver assistance systems and autonomous driving. For object detection and measurements at large distances stereo vision encounter difficulties. This includes objects being small, having low contrast and the presence of image noise. Having an accurate perception of the environment at large distances is however of high interest for many applications, especially autonomous driving. This thesis proposes a method which tries to increase the range to where generic objects are first detected using a given stereo camera setup. Objects are represented by planes in 3D space. The input image is segmented into the various objects and the 3D plane parameters are estimated jointly. The 3D plane parameters are estimated directly from the stereo image pairs. In particular, this thesis investigates methods to introduce geometric constraints to the segmentation or labeling task, i.e assigning each considered pixel in the image to a plane. The methods provided in this thesis show that despite the difficulties at large distances it is possible to exploit planar primitives in 3D space for obstacle detection at distances where other methods fail. / En autonom bil innebär att bilen har en uppfattning om sin omgivning och kan utifran det ta beslut angående hur bilen ska manövreras. Konceptet med självkörande bilar har existerat i årtionden men har utvecklats snabbt senaste åren sedan billigare datorkraft finns lättare tillgänglig. Fördelar med autonomiska bilar innebär bland annat att antalet olyckor i trafiken minskas och därmed liv räddas. En av de största utmaningarna med autonoma bilar är att få 3D information och relationer mellan objekt som finns i den omgivande trafikmiljön. Detta kallas för spatial perception och innebär att detektera alla objekt och tilldela en korrekt postition till dem. Stereo kamerasystem har fått en central roll för avancerade förarsystem och autonoma bilar. För detektion av objekt på stora avstånd träffar stereo system på svårigheter. Detta inkluderar väldigt små objekt, låg kontrast och närvaron av brus i bilden. Att ha en ackurativ perception på stora avstånd är dock vitalt för många applikationer, inte minst autonoma bilar. Den här rapporten föreslar en metod som försöker öka avståndet till där objekt först upptäcks. Objekt representeras av plan i 3D rymden. Bilder givna från stereo par segmenteras i olika object och plan parametrar estimeras samtidigt. Planens parametrar estimeras direkt från stereo bild paren. Den här rapporten utreder metoder att introducera gemoetriska begränsningar att använda vid segmenteringsuppgiften. Metoderna som presenteras i denna rapport visar att trots den höga närvaron av brus på stora avstånd är det möjligt att estimera geometriska objekt som är starka nog att möjliggöra detektion av objekt på ett avstand där andra metoder misslyckas.

Alloy is a lightweight formal modeling tool that generates instances of a software specification to check properties of the design. This work demonstrates the use of Alloy for the rapid development of autonomous vehicle driving protocols. We contribute two driving protocols: a Normal protocol that represents the unpredictable yet safe driving behavior of typical human drivers, and a Connected protocol that employs connected technology for cooperative autonomous driving. Using five properties that define safe and productive driving actions, we analyze the performance of our protocols in mixed traffic. Lightweight formal modeling is a valuable way to reason about driving protocols early in the development process because it can automate the checking of safety and productivity properties and prevent costly design flaws.

No description available.

Indiana University-Purdue University Indianapolis (IUPUI) / The current technologies employed for autonomous driving provide tremendous performance and results, but the technology itself is far from mature and relatively expensive. Some of the most commonly used components for autonomous driving include LiDAR, cameras, radar, and ultrasonic sensors. Sensors like such are usually high-priced and often require a tremendous amount of computational power in order to process the gathered data. Many car manufacturers consider cameras to be a low-cost alternative to some other costly sensors, but camera based sensors alone are prone to fatal perception errors. In many cases, adverse weather and night-time conditions hinder the performance of some vision based sensors. In order for a sensor to be a reliable source of data, the difference between actual data values and measured or perceived values should be as low as possible. Lowering the number of sensors used provides more economic freedom to invest in the reliability of the components used. This thesis provides an alternative approach to the current autonomous driving methodologies by utilizing acoustic signatures of moving objects. This approach makes use of a microphone array to collect and process acoustic signatures captured for simultaneous localization and mapping (SLAM). Rather than using numerous sensors to gather information about the surroundings that are beyond the reach of the user, this method investigates the benefits of considering the sound waves of different objects around the host vehicle for SLAM. The components used in this model are cost-efficient and generate data that is easy to process without requiring high processing power. The results prove that there are benefits in pursuing this approach in terms of cost efficiency and low computational power. The functionality of the model is demonstrated using MATLAB for data collection and testing.

Traffic congestion and safety has become a major issue in the modern world's commute. Congestion has been causing people to travel billions of hours more and to purchase billions of gallons of fuel extra which account to congestion cost of billions of dollars. Autonomous driving vehicles have been one solution to this problem because of their huge impact on efficiency, pollution, and human safety. Also, extensive research has been carried out on control design of vehicular platoons because a further improvement in traffic throughput while not compromising the safety is possible when the vehicles in the platoon are provided with better predictive abilities. Motion control is a key area of autonomous driving research that handles moving parts of vehicles in a deliberate and controlled manner. A widely worked on problem in motion control concerned with time parameterized reference tracking is trajectory tracking. Having an efficient and effective tracking algorithm embedded in the autonomous driving system is the key for better performance in terms of resources consumed and tracking error. Many tracking control algorithms in literature rely on an accurate model of the vehicle and often, it can be an intimidating task to come up with an accurate model taking into consideration various conditions like friction, heat effects, ageing processes etc. And typically, control algorithms rely on periodic execution of the tasks that update the control actions, but such updates might not be required, which result in unnecessary actions that waste resources. The main focus of this work is to design an intermittent model-free optimal control algorithm in order to enable autonomous vehicles to track trajectories at high-speeds. To obtain a solution which is model-free, a Q-learning setup with an actor-network to approximate the optimal intermittent controller and a critic network to approximate the optimal cost, resulting in the appropriate tuning laws is considered. / Master of Science / A risen research effort in the area of autonomous vehicles has been witnessed in the past few decades because these systems improve safety, comfort, transport time and energy consumption which are some of the main issues humans are facing in the modern world’s highway systems. Systems like emergency braking, automatic parking, blind angle vehicle detection are creating a safer driving environment in populated areas. Advanced driver assistance systems (ADAS) are what such kind of systems are known as. An extension of these partially automated ADAS are vehicles with fully automated driving abilities, which are able to drive by themselves without any human involvement. An extensively proposed approach for making traffic throughput more efficient on existing highways is to assemble autonomous vehicles into platoons. Small intervehicle spacing and many vehicles constituting each platoon formation improve the traffic throughput significantly. Lately, the advancements in computational capabilities, in terms of both algorithms and hardware, communications, and navigation and sensing devices contributed a lot to the development of autonomous systems (both single and multiagent) that operate with high reliability in uncertain/dynamic operating conditions and environments. Motion control is an important area in the autonomous vehicles research. Trajectory-tracking is a widely studied motion control scenario which is about designing control laws that force a system to follow some time-dependent reference path and it is important to have an effective and efficient trajectory-tracking control law in an autonomous vehicle to reduce the resources consumed and tracking error. The goal of this work is to design an intermittent model-free trajectory tracking control algorithm where there is no need of any mathematical model of the vehicle system being controlled and which can reduce the controller updates by allowing the system to evolve in an open loop fashion and close the loop only when an user defined triggering condition is satisfied. The approach is energy efficient in that the control updates are limited to instances when they are needed rather than unnecessary periodic updates. Q-learning which is a model-free reinforcement learning technique is used in the trajectory tracking motion control algorithm to make the vehicles track their respective reference trajectories without any requirement of their motion model, the knowledge of which is generally needed when dealing with a motion control problem. The testing of the designed algorithm in simulations and experiments is presented in this work. The study and development of a vehicle platform in order to perform the experiments is also discussed. Different motion control and sensing techniques are presented and used. The vehicle platform is shown to track a reference trajectory autonomously without any human intervention, both in simulations and experiments, proving the effectiveness of the proposed algorithm.

<p dir="ltr">The growth of electric vehicles (EVs) offers several benefits for air quality improvement and emissions reduction. Nonetheless, EVs also pose several challenges in the area of highway transportation. These barriers are related to the limitations of EV technology, particularly the charge duration and speed of battery recharging, which translate to vehicle range anxiety for EV users. A promising solution to these concerns is V2V DWC technology (Vehicle to Vehicle Dynamic Wireless Charging), particularly mobile energy disseminators (MEDs). The MED is mounted on a large vehicle or truck that charges all participating EVs within a specified locus from the MED. However, current research on MEDs offers solutions that are widely considered impractical for deployment, particularly in urban environments where range anxiety is common. Acknowledging such gap in the literature, this thesis proposes a comprehensive methodological framework for optimal MED deployment decisions. In the first component of the framework, a practical system, termed “ChargingEnv” is developed using reinforcement learning (RL). ChargingEnv simulates the highway environment, which consists of streams of EVs and an MED. The simulation accounts for a possible misalignment of the charging panel and incorporates a realistic EV battery model. The second component of the framework uses multiple deep RL benchmark models that are trained in “ChargingEnv” to maximize EV service quality within limited charging resource constraints. In this study, numerical experiments were conducted to demonstrate the MED deployment decision framework’s efficacy. The findings indicate that the framework’s trained model can substantially improve EV travel range and alleviate battery depletion concerns. This could serve as a vital tool that allows public-sector road agencies or private-sector commercial entities to efficiently orchestrate MED deployments to maximize service cost-effectiveness.</p>

Vehicle-to-Vehicle communication is the key technology for achieving increased perception for automated vehicles where the communication allows virtual sensing with the use of sensors placed in other vehicles. In addition, this technology also allows recognising objects that are out-of-sight. This thesis presents a Trust System that allows a vehicle to make more reliable and robust decisions. The system evaluates the current situation and generates a Trust Index indicating the level of trust in the environment, the ego vehicle, and the other vehicles. Current research focuses on securing the communication between the vehicles themselves, but does not verify the content of the received data on a system level. The proposed Trust System evaluates the received data according to sensor accuracy, behaviour of other vehicles, and the perception of the local environment. The results show that the proposed method is capable of correctly identifying various situations and discusses how the Trust Index can be used to make more robust decisions.

This thesis is concerned with the problem of situational awareness in autonomous vehicles. In this context, situational awareness refers to the ability of an autonomous vehicle to perceive the road layout ahead, interpret the implied semantics and gain an awareness of its surrounding - thus reading the road ahead. Autonomous vehicles require a high level of situational awareness in order to operate safely and efficiently in real-world dynamic environments. A system is therefore needed that is able to model the expected road layout in terms of semantics, both under normal and roadwork conditions. This thesis takes a three-pronged approach to this problem: Firstly, we consider reading the road surface. This is formulated in terms of probabilistic road marking classification and interpretation. We then derive the road boundaries using only a 2D laser and algorithms based on geometric priors from Highway Traffic Engineering principles. Secondly, we consider reading the road scene. Here, we formulate a roadwork scene recognition framework based on opponent colour vision in humans. Finally, we provide a data representation for situational awareness that unifies reading the road surface and reading the road scene. This thesis therefore frames situational awareness in autonomous vehicles in terms of both static and dynamic road semantics - and detailed formulations and algorithms are discussed. We test our algorithms on several benchmarking datasets collected using our autonomous vehicle on both rural and urban roads. The results illustrate that our road boundary estimation, road marking classification, and roadwork scene recognition frameworks allow autonomous vehicles to truly and meaningfully read the semantics of the road ahead, thus gaining a valuable sense of situational awareness even at challenging layouts, roadwork sites, and along unknown roadways.