Making Transport Safer: V2V-Based Automated Emergency Braking System

An important goal in the field of intelligent transportation systems (ITS) is to provide driving aids aimed at preventing accidents and reducing the number of traffic victims. The commonest traffic accidents in urban areas are due to sudden braking that demands a very fast response on the part of drivers. Attempts to solve this problem have motivated many ITS advances including the detection of the intention of surrounding cars using lasers, radars or cameras. However, this might not be enough to increase safety when there is a danger of collision. Vehicle to vehicle communications are needed to ensure that the other intentions of cars are also available. The article describes the development of a controller to perform an emergency stop via an electro-hydraulic braking system employed on dry asphalt. An original V2V communication scheme based on WiFi cards has been used for broadcasting positioning information to other vehicles. The reliability of the scheme has been theoretically analyzed to estimate its performance when the number of vehicles involved is much higher. This controller has been incorporated into the AUTOPIA program control for automatic cars. The system has been implemented in Citroën C3 Pluriel, and various tests were performed to evaluate its operation

Benets of tight coupled architectures for the integration of GNSS receiver and Vanet transceiver

Vehicular adhoc networks (VANETs) are one emerging type of networks that will enable a broad range of applications such as public safety, traffic management, traveler information support and entertain ment. Whether wireless access may be asynchronous or synchronous (respectively as in the upcoming IEEE 8021.11p standard or in some alternative emerging solutions), a synchronization among nodes is required. Moreover, the information on position is needed to let vehicular services work and to correctly forward the messages. As a result, timing and positioning are a strong prerequisite of VANETs. Also the diffusion of enhanced GNSS Navigators paves the way to the integration between GNSS receivers and VANET transceiv ers. This position paper presents an analysis on potential benefits coming from a tightcoupling between the two: the dissertation is meant to show to what extent Intelligent Transportation System (ITS) services could benefit from the proposed architectur

Systems and methods for intersection management of connected autonomous vehicles

Various embodiments of an intersection management system for managing autonomous vehicles approaching an intersection in which a Time of Arrival, Velocity of Arrival, and path trajectory are calculated for each approaching vehicle are disclosed

Security in Vehicles With IoT by Prioritization Rules, Vehicle Certificates, and Trust Management

[EN] The Internet of Vehicles (IoV) provides new opportunities for the coordination of vehicles for enhancing safety and transportation performance. Vehicles can be coordinated for avoiding collisions by communicating their positions when near to each other, in which the information flow is indexed by their geographical positions or the ones in road maps. Vehicles can also be coordinated to ameliorate traffic jams by sharing their locations and destinations. Vehicles can apply optimization algorithms to reduce the overuse of certain streets without excessively enlarging the paths. In this way, traveling time can be reduced. However, IoV also brings security challenges, such as keeping safe from virtual hijacking. In particular, vehicles should detect and isolate the hijacked vehicles ignoring their communications. The current work presents a technique for enhancing security by applying certain prioritization rules, using digital certificates, and applying trust and reputation policies for detecting hijacked vehicles. We tested the proposed approach with a novel agent-based simulator about security in Internet of Things (IoT) for vehicle-to-vehicle communications. The experiments focused on the scenario of avoidance of collisions with hijacked vehicles misinforming other vehicles. The results showed that the current approach increased the average speed of vehicles with a 64.2% when these are giving way to other vehicles in a crossing by means of IoT.This work was supported by Harvard University (stay funded by T49_17R), University of Zaragoza (JIUZ-2017-TEC-03), Foundation Bancaria Ibercaja, Foundation CAI (IT1/18), University Foundation Antonio Gargallo (call 2017), and "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" (TIN2017-84802-C2-1-P).García-Magariño, I.; Sendra, S.; Lacuesta, R.; Lloret, J. (2019). Security in Vehicles With IoT by Prioritization Rules, Vehicle Certificates, and Trust Management. IEEE Internet of Things. 6(4):5927-5934. https://doi.org/10.1109/JIOT.2018.2871255S592759346

Autonomous vehicle control systems for safe crossroads

This article presents a cooperative manoeuvre among three dual mode cars – vehicles equipped with sensors and actuators, and that can be driven either manually or autonomously. One vehicle is driven autonomously and the other two are driven manually. The main objective is to test two decision algorithms for priority conflict resolution at intersections so that a vehicle autonomously driven can take their own decision about crossing an intersection mingling with manually driven cars without the need for infrastructure modifications. To do this, the system needs the position, speeds, and turning intentions of the rest of the cars involved in the manoeuvre. This information is acquired via communications, but other methods are also viable, such as artificial vision. The idea of the experiments was to adjust the speed of the manually driven vehicles to force a situation where all three vehicles arrive at an intersection at the same time

Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning

Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers. We explore the effectiveness of Deep Reinforcement Learning to handle intersection problems. Using recent advances in Deep RL, we are able to learn policies that surpass the performance of a commonly-used heuristic approach in several metrics including task completion time and goal success rate and have limited ability to generalize. We then explore a system's ability to learn active sensing behaviors to enable navigating safely in the case of occlusions. Our analysis, provides insight into the intersection handling problem, the solutions learned by the network point out several shortcomings of current rule-based methods, and the failures of our current deep reinforcement learning system point to future research directions.Comment: IEEE International Conference on Robotics and Automation (ICRA 2018

Compensation at the Crossroads: Autonomous Vehicles & Alternative Victim Compensation Schemes

Fully autonomous vehicles will become available to consumers within the next five to seven years. Experts predict that these vehicles will be drastically safer than their human-driven counterparts and will save thousands of lives each year in the United States alone. However, crashes will still occur, and when they do, they will raise unique and troubling issues about liability and fault that both negligence and products liability jurisprudence are not yet wellsuited to handle. Whether the civil justice system can adjudicate autonomous vehicle crash cases fairly and efficiently impacts (a) whether manufacturers can afford to produce these vehicles or whether the cost and magnitude of litigation surrounding them will destroy their market, (b) whether consumers will adopt this new technology, and (c) the rate at which they will be willing and able to do so. These issues, in turn, have an impact on how many lives can be saved on U.S. roads each year. It is thus imperative to design a method of compensating victims, protecting manufacturers, and giving courts time and space to develop jurisprudence applicable to this technology if we wish to reap the profound benefits that fully autonomous vehicles stand to offer. Although filing a lawsuit in the civil justice system will always be an option for victims of autonomous vehicle crashes, a specially designed, no-fault victim compensation fund offers a sensible way to address the issues identified above and to resolve these cases in a faster and less costly manner. While the use of victim compensation funds is a fairly recent phenomenon in the United States, these funds have been used with great success in a variety of situations and could be used successfully here. In the model proposed in this paper, an autonomous vehicle crash victim compensation fund would be administered by the National Highway Traffic Safety Administration (NHTSA) and financed by a tax levied on the sale of all fully autonomous vehicles. Victims who wish to seek compensation from the fund would be able to do so via a simple claim form and an agreement to waive their right to sue. Manufacturers, in turn, would be required to participate in a datasharing and design improvement program as a condition of receiving protection from the fund. This program would both assist NHTSA in gathering the information it needs to regulate autonomous vehicles and reduce the likelihood that a victim compensation fund would undermine manufacturer incentives to improve the safety of their vehicles. Participation by both victims and manufacturers would be voluntary, but the benefits of entering the fund would likely induce high levels of participation from both

Fine-Grained Reliability for V2V Communications around Suburban and Urban Intersections

Safe transportation is a key use-case of the 5G/LTE Rel.15+ communications, where an end-to-end reliability of 0.99999 is expected for a vehicle-to-vehicle (V2V) transmission distance of 100-200 m. Since communications reliability is related to road-safety, it is crucial to verify the fulfillment of the performance, especially for accident-prone areas such as intersections. We derive closed-form expressions for the V2V transmission reliability near suburban corners and urban intersections over finite interference regions. The analysis is based on plausible street configurations, traffic scenarios, and empirically-supported channel propagation. We show the means by which the performance metric can serve as a preliminary design tool to meet a target reliability. We then apply meta distribution concepts to provide a careful dissection of V2V communications reliability. Contrary to existing work on infinite roads, when we consider finite road segments for practical deployment, fine-grained reliability per realization exhibits bimodal behavior. Either performance for a certain vehicular traffic scenario is very reliable or extremely unreliable, but nowhere in relatively proximity to the average performance. In other words, standard SINR-based average performance metrics are analytically accurate but can be insufficient from a practical viewpoint. Investigating other safety-critical point process networks at the meta distribution-level may reveal similar discrepancies.Comment: 27 pages, 6 figures, submitted to IEEE Transactions on Wireless Communication

FABRIC: A National-Scale Programmable Experimental Network Infrastructure

FABRIC is a unique national research infrastructure to enable cutting-edge and exploratory research at-scale in networking, cybersecurity, distributed computing and storage systems, machine learning, and science applications. It is an everywhere-programmable nationwide instrument comprised of novel extensible network elements equipped with large amounts of compute and storage, interconnected by high speed, dedicated optical links. It will connect a number of specialized testbeds for cloud research (NSF Cloud testbeds CloudLab and Chameleon), for research beyond 5G technologies (Platforms for Advanced Wireless Research or PAWR), as well as production high-performance computing facilities and science instruments to create a rich fabric for a wide variety of experimental activities