Fault-Tolerance by Graceful Degradation for Car Platoons

The key advantage of autonomous car platoons are their short inter-vehicle distances that increase traffic flow and reduce fuel consumption. However, this is challenging for operational and functional safety. If a failure occurs, the affected vehicles cannot suddenly stop driving but instead should continue their operation with reduced performance until a safe state can be reached or, in the case of temporal failures, full functionality can be guaranteed again. To achieve this degradation, platoon members have to be able to compensate sensor and communication failures and have to adjust their inter-vehicle distances to ensure safety. In this work, we describe a systematic design of degradation cascades for sensor and communication failures in autonomous car platoons using the example of an autonomous model car. We describe our systematic design method, the resulting degradation modes, and formulate contracts for each degradation level. We model and test our resulting degradation controller in Simulink/Stateflow

Fully automated urban traffic system

The replacement of the driver with an automatic system which could perform the functions of guiding and routing a vehicle with a human's capability of responding to changing traffic demands was discussed. The problem was divided into four technological areas; guidance, routing, computing, and communications. It was determined that the latter three areas being developed independent of any need for fully automated urban traffic. A guidance system that would meet system requirements was not being developed but was technically feasible

Automated highway systems : platoons of vehicles viewed as a multiagent system

Tableau d'honneur de la Faculté des études supérieures et postdoctorales, 2005-2006La conduite collaborative est un domaine lié aux systèmes de transport intelligents, qui utilise les communications pour guider de façon autonome des véhicules coopératifs sur une autoroute automatisée. Depuis les dernières années, différentes architectures de véhicules automatisés ont été proposées, mais la plupart d’entre elles n’ont pas, ou presque pas, attaqué le problème de communication inter véhicules. À l’intérieur de ce mémoire, nous nous attaquons au problème de la conduite collaborative en utilisant un peloton de voitures conduites par des agents logiciels plus ou moins autonomes, interagissant dans un même environnement multi-agents: une autoroute automatisée. Pour ce faire, nous proposons une architecture hiérarchique d’agents conducteurs de voitures, se basant sur trois couches (couche de guidance, couche de management et couche de contrôle du trafic). Cette architecture peut être utilisée pour développer un peloton centralisé, où un agent conducteur de tête coordonne les autres avec des règles strictes, et un peloton décentralisé, où le peloton est vu comme une équipe d’agents conducteurs ayant le même niveau d’autonomie et essayant de maintenir le peloton stable.Collaborative driving is a growing domain of Intelligent Transportation Systems (ITS) that makes use of communications to autonomously guide cooperative vehicles on an Automated Highway System (AHS). For the past decade, different architectures of automated vehicles have been proposed, but most of them did not or barely addressed the inter-vehicle communication problem. In this thesis, we address the collaborative driving problem by using a platoon of cars driven by more or less autonomous software agents interacting in a Multiagent System (MAS) environment: the automated highway. To achieve this, we propose a hierarchical driving agent architecture based on three layers (guidance layer, management layer and traffic control layer). This architecture can be used to develop centralized platoons, where the driving agent of the head vehicle coordinates other driving agents by applying strict rules, and decentralized platoons, where the platoon is considered as a team of driving agents with a similar degree of autonomy, trying to maintain a stable platoon

Odometry and Laser Scanner Fusion Based on a Discrete Extended Kalman Filter for Robotic Platooning Guidance

This paper describes a relative localization system used to achieve the navigation of a convoy of robotic units in indoor environments. This positioning system is carried out fusing two sensorial sources: (a) an odometric system and (b) a laser scanner together with artificial landmarks located on top of the units. The laser source allows one to compensate the cumulative error inherent to dead-reckoning; whereas the odometry source provides less pose uncertainty in short trajectories. A discrete Extended Kalman Filter, customized for this application, is used in order to accomplish this aim under real time constraints. Different experimental results with a convoy of Pioneer P3-DX units tracking non-linear trajectories are shown. The paper shows that a simple setup based on low cost laser range systems and robot built-in odometry sensors is able to give a high degree of robustness and accuracy to the relative localization problem of convoy units for indoor applications

VANET Applications: Hot Use Cases

Current challenges of car manufacturers are to make roads safe, to achieve free flowing traffic with few congestions, and to reduce pollution by an effective fuel use. To reach these goals, many improvements are performed in-car, but more and more approaches rely on connected cars with communication capabilities between cars, with an infrastructure, or with IoT devices. Monitoring and coordinating vehicles allow then to compute intelligent ways of transportation. Connected cars have introduced a new way of thinking cars - not only as a mean for a driver to go from A to B, but as smart cars - a user extension like the smartphone today. In this report, we introduce concepts and specific vocabulary in order to classify current innovations or ideas on the emerging topic of smart car. We present a graphical categorization showing this evolution in function of the societal evolution. Different perspectives are adopted: a vehicle-centric view, a vehicle-network view, and a user-centric view; described by simple and complex use-cases and illustrated by a list of emerging and current projects from the academic and industrial worlds. We identified an empty space in innovation between the user and his car: paradoxically even if they are both in interaction, they are separated through different application uses. Future challenge is to interlace social concerns of the user within an intelligent and efficient driving

Safety impact of connected and autonomous vehicles on motorways: a traffic microsimulation study

Connected and Autonomous Vehicles (CAVs) promise to improve road safety greatly. Despite the numerous CAV trials around the globe, their benefit has yet to be proven using real-world data. The lack of real-world CAV data has shifted the focus of the research community from traditional safety impact assessment methods to traffic microsimulation in order to evaluate their impacts. However, a plethora of operational, tactical and strategic challenges arising from the implementation of CAV technology remain unaddressed. This thesis presents an innovative and integrated CAV traffic microsimulation framework that aims to cover the aforementioned shortcomings.A new CAV control algorithm is developed in C++ programming language containing a longitudinal and lateral control algorithm that for the first time takes into consideration sensor error and vehicle platoon formulation of various sizes. A route-based decision-making algorithm for CAVs is also developed. The algorithm is applied to a simulated network of the M1 motorway in the United Kingdom which is calibrated and validated using instrumented vehicle data and inductive loop detector data. Multiple CAV market penetration rate, platoon size and sensor error rate scenarios are formulated and evaluated. Safety evaluation is conducted using traffic conflicts as a safety surrogate measure which is a function of time-to-collision and post encroachment time. The results reveal significant safety benefit (i.e. 10-94% reduction of traffic conflicts) as CAV market penetration increases from 0% to 100%; however, it is underlined that special focus should be given in the motorway merging and diverging areas where CAVs seem to face the most challenges. Additionally, it is proven that if the correct CAV platoon size is implemented at the appropriate point in time, greater safety benefits may be achieved. Otherwise, safety might deteriorate. However, sensor error does not affect traffic conflicts for the studied network. These results could provide valuable insights to policy makers regarding the reconfiguration of existing infrastructure to accommodate CAVs, the trustworthiness of existing CAV equipment and the optimal platoon size that should be enforced according to the market penetration rate.Finally, in order to forecast the conflict reduction for any given market penetration rate and understand the underlying factors behind traffic conflicts in a traffic microsimulation environment in-depth, a hierarchical spatial Bayesian negative binomial regression model is developed, based on the simulated CAV data. The results exhibit that besides CAV market penetration rate, speed variance across lanes significantly affects the production of simulated conflicts. As speed variance increases, the safety benefit decreases. These results emphasize the importance of speed homogeneity between lanes in a motorway as well as the increased risk in the motorway merging/diverging areas.</div

Exploring Smart Infrastructure Concepts to Improve the Reliability and Functionality of Safety Oriented Connected Vehicle Applications

Cooperative adaptive cruise control (CACC), a form of vehicle platooning, is a well known connected vehicle application. It extends adaptive cruise control (ACC) by incorporating vehicle-to-vehicle communications. A vehicle periodically broadcasts a small message that includes in the least a unique vehicle identifier, its current geo-location, speed, and acceleration. A vehicle might pay attention to the message stream of only the car ahead. While CACC is under intense study by the academic community, the vast majority of the relevant published literature has been limited to theoretical studies that make many simplifying assumptions. The research presented in this dissertation has been motivated by our observation that there is limited understanding of how platoons actually work under a range of realistic operating conditions. Our research includes a performance study of V2V communications based on actual V2V radios supplemented by simulation. These results are in turn applied to the analysis of CACC. In order to understand a platoon at scale, we resort to simulations and analysis using the ns3 simulator. Assessment criteria includes network reliability measures as well as application oriented measures. Network assessment involves latency and first and second order loss dynamics. CACC performance is based on stability, frequency of crashes, and the rate of traffic flow. The primary goal of CACC is to maximize traffic flow subject to a maximum allowed speed. This requires maintaining smaller inter-vehicle distances which can be problematic as a platoon can become unstable as the target headway between cars is reduced. The main contribution of this dissertation is the development and evaluation of two heuristic approaches for dynamically adapting headway both of which attempt to minimize the headway while ensure stability. We present the design and analysis of a centralized and a distributed implementation of the algorithm. Our results suggest that dynamically adapting the headway time can improve the overall platoon traffic flow without the platoon becoming unstable

Ground Vehicle Platooning Control and Sensing in an Adversarial Environment

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

Assessing the effectiveness of managed lane strategies for the rapid deployment of cooperative adaptive cruise control technology

Connected and Automated Vehicle (C/AV) technologies are fast expanding in the transportation and automotive markets. One of the highly researched examples of C/AV technologies is the Cooperative Adaptive Cruise Control (CACC) system, which exploits various vehicular sensors and vehicle-to-vehicle communication to automate vehicular longitudinal control. The operational strategies and network-level impacts of CACC have not been thoroughly discussed, especially in near-term deployment scenarios where Market Penetration Rate (MPR) is relatively low. Therefore, this study aims to assess CACC\u27s impacts with a combination of managed lane strategies to provide insights for CACC deployment. The proposed simulation framework incorporates 1) the Enhanced Intelligent Driver Model; 2) Nakagami-based radio propagation model; and 3) a multi-objective optimization (MOOP)-based CACC control algorithm. The operational impacts of CACC are assessed under four managed lane strategies (i.e., mixed traffic (UML), HOV (High Occupancy Vehicle)-CACC lane (MML), CACC dedicated lane (DL), and CACC dedicated lane with access control (DLA)). Simulation results show that the introduction of CACC, even with 10% MPR, is able to improve the network throughput by 7% in the absence of any managed lane strategies. The segment travel times for both CACC and non-CACC vehicles are reduced. The break-even point for implementing dedicated CACC lane is 30% MPR, below which the priority usage of the current HOV lane for CACC traffic is found to be more appropriate. It is also observed that DLA strategy is able to consistently increase the percentage of platooned CACC vehicles as MPR grows. The percentage of CACC vehicles within a platoon reaches 52% and 46% for DL and DLA, respectively. When it comes to the impact of vehicle-to-vehicle (V2V), it is found that DLA strategy provides more consistent transmission density in terms of median and variance when MPR reaches 20% or above. Moreover, the performance of the MOOP-based cooperative driving is examined. With average 75% likelihood of obtaining a feasible solution, the MOOP outperforms its counterpart which aims to minimize the headway objective solely. In UML, MML, and DL strategy, the proposed control algorithm achieves a balance spread among four objectives for each CACC vehicle. In the DLA strategy, however, the probability of obtaining feasible solution falls to 60% due to increasing size of platoon owing to DLA that constraints the feasible region by introduction more dimensions in the search space. In summary, UML or MML is the preferred managed lane strategy for improving traffic performance when MPR is less than 30%. When MRP reaches 30% or above, DL and DLA could improve the CACC performance by facilitating platoon formation. If available, priority access to an existing HOV lane can be adopted to encourage adaptation of CACC when CACC technology becomes publically available