A Probabilistic Model for AVCS Longitudinal Collision/Safety Analysis

This paper develops a probabilistic model for analyzing longitudinal collision/safety between an abruptly decelerating vehicle and its immediate follower. The input parameters are the length of the gap between the two vehicles, their common speed prior to the failure, the reaction delay of the following vehicle and a bivariate distribution for the two deceleration rates. The output includes the probability of a collision and the probability distribution of the relative speed at collision time We use this model to compare the safety consequences associated with the platooning and “free-agent” longitudinal-separation rules. We also demonstrate that the free-agent rule implemented with a potential technology of fast and accurate emergency deceleration, under some reasonable conditions, can avoid collisions while offering a high freeway capacity previously thought possible only under the platooning rule. This model has many other applications

Cognitive Vehicle Platooning in the Era of Automated Electric Transportation

Vehicle platooning is an important innovation in the automotive industry that aims at improving safety, mileage, efficiency, and the time needed to travel. This research focuses on the various aspects of vehicle platooning, one of the important aspects being analysis of different control strategies that lead to a stable and robust platoon. Safety of passengers being a very important consideration, the control design should be such that the controller remains robust under uncertain environments. As a part of the Department of Energy (DOE) project, this research also tries to show a demonstration of vehicle platooning using robots. In an automated highway scenario, a vehicle platoon can be thought of as a string of vehicles, following one another as a platoon. Being equipped by wireless communication capabilities, these vehicles communicate with one another to maintain their formation as a platoon, hence are cognitive. Autonomous capable vehicles in tightly spaced, computer-controlled platoons will lead to savings in energy due to reduced aerodynamic forces, as well as increased passenger comfort since there will be no sudden accelerations or decelerations. Impacts in the occurrence of collisions, if any, will be very low. The greatest benefit obtained is, however, an increase in highway capacity, along with reduction in traffic congestion, pollution, and energy consumption. Another aspect of this project is the automated electric transportation (AET). This aims at providing energy directly to vehicles from electric highways, thus reducing their energy consumption and CO2 emission. By eliminating the use of overhead wires, infrastructure can be upgraded by electrifying highways and providing energy on demand and in real time to moving vehicles via a wireless energy transfer phenomenon known as wireless inductive coupling. The work done in this research will help to gain an insight into vehicle platooning and the control system related to maintaining the vehicles in this formation

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

Cooperative adaptive cruise control : a learning approach

Tableau d’honneur de la Faculté des études supérieures et postdoctorales, 2008-2009L'augmentation dans les dernières décennies du nombre de véhicules présents sur les routes ne s'est pas passée sans son lot d'impacts négatifs sur la société. Même s'ils ont joué un rôle important dans le développement économique des régions urbaines à travers le monde, les véhicules sont aussi responsables d'impacts négatifs sur les entreprises, car l'inefficacité du ot de traffic cause chaque jour d'importantes pertes en productivité. De plus, la sécurité des passagers est toujours problématique car les accidents de voiture sont encore aujourd'hui parmi les premières causes de blessures et de morts accidentelles dans les pays industrialisés. Ces dernières années, les aspects environnementaux ont aussi pris de plus en plus de place dans l'esprit des consommateurs, qui demandent désormais des véhicules efficaces au niveau énergétique et minimisant leurs impacts sur l'environnement. évidemment, les gouvernements de pays industrialisés ainsi que les manufacturiers de véhicules sont conscients de ces problèmes et tentent de développer des technologies capables de les résoudre. Parmi les travaux de recherche en ce sens, le domaine des Systèmes de Transport Intelligents (STI) a récemment reçu beaucoup d'attention. Ces systèmes proposent d'intégrer des systèmes électroniques avancés dans le développement de solutions intelligentes conçues pour résoudre les problèmes liés au transport automobile cités plus haut. Ce mémoire se penche donc sur un sous-domaine des STI qui étudie la résolution de ces problèmes gr^ace au développement de véhicules intelligents. Plus particulièrement, ce mémoire propose d'utiliser une approche relativement nouvelle de conception de tels systèmes, basée sur l'apprentissage machine. Ce mémoire va donc montrer comment les techniques d'apprentissage par renforcement peuvent être utilisées afin d'obtenir des contrôleurs capables d'effectuer le suivi automatisés de véhicules. Même si ces efforts de développement en sont encore à une étape préliminaire, ce mémoire illustre bien le potentiel de telles approches pour le développement futur de véhicules plus \intelligents".The impressive growth, in the past decades, of the number of vehicles on the road has not come without its share of negative impacts on society. Even though vehicles play an active role in the economical development of urban regions around the world, they unfortunately also have negative effects on businesses as the poor efficiency of the traffic ow results in important losses in productivity each day. Moreover, numerous concerns have been raised in relation to the safety of passengers, as automotive transportation is still among the first causes of accidental casualties in developed countries. In recent years, environmental issues have also been taking more and more place in the mind of customers, that now demand energy-efficient vehicles that limit the impacts on the environment. Of course, both the governments of industrialized countries and the vehicle manufacturers have been aware of these problems, and have been trying to develop technologies in order to solve these issues. Among these research efforts, the field of Intelligent Transportation Systems (ITS) has been gathering much interest as of late, as it is considered an efficient approach to tackle these problems. ITS propose to integrate advanced electronic systems in the development of intelligent solutions designed to address the current issues of automotive transportation. This thesis focuses on a sub-field ITS since it studies the resolution of these problems through the development of Intelligent Vehicle (IV) systems. In particular, this thesis proposes a relatively novel approach for the design of such systems, based on modern machine learning. More specifically, it shows how reinforcement learning techniques can be used in order to obtain an autonomous vehicle controller for longitudinal vehiclefollowing behavior. Even if these efforts are still at a preliminary stage, this thesis illustrates the potential of using these approaches for future development of \intelligent" vehicles

Ground vehicle control at NIST: From teleoperation to autonomy

NIST is applying their Real-time Control System (RCS) methodology for control of ground vehicles for both the U.S. Army Researh Lab, as part of the DOD's Unmanned Ground Vehicles program, and for the Department of Transportation's Intelligent Vehicle/Highway Systems (IVHS) program. The actuated vehicle, a military HMMWV, has motors for steering, brake, throttle, etc. and sensors for the dashboard gauges. For military operations, the vehicle has two modes of operation: a teleoperation mode--where an operator remotely controls the vehicle over an RF communications network; and a semi-autonomous mode called retro-traverse--where the control system uses an inertial navigation system to steer the vehicle along a prerecorded path. For the IVHS work, intelligent vision processing elements replace the human teleoperator to achieve autonomous, visually guided road following

Analytical Models for Vehicle/Gap Distribution on Automated Highway Systems

Highway congestion has in recent years become a pervasive problem for urban and suburban areas alike. The concept of Automated Highway Systems is based on the belief that integration of sensing, communication, and control technologies into vehicles and highways can lead to a large improvement in capacity and safety without requiring a significant amount of additional highway right-of-way. A fundamental determinant of Automated Highway Systems capacity is the vehicle-following rule, the rule that governs the behavior of vehicles traveling along a common lane (e.g., the spacing between any two longitudinally adjacent vehicles). Vehicle following affects the longitudinal capacity (achievable flow within a lane), the lateral capacity (achievable flow between lanes) and the conflicting relationship between the longitudinal flow and lateral capacity. The issues are investigated by developing probabilistic models for vehicle/platoon and gap distributions, for vehicles that travel in platoons, in slots, or as free-agents. Mathematical models are also developed to estimate the completion time of a lane change, which can be used as a surrogate for the lateral capacity. Numerical results for the three major vehicle-following rules and their comparison are also provided

Entrance Capacity of an Automated Highway System

This paper evaluates the entrance capacity and queueing delay for Automated Highway Systems through use of simulations and analytical modeling. Queueing statistics are also used to determine the sustainable capacity of alternative concepts, taking trip length distribution and spacing between ramps into consideration. Based on safety-spacing headways (produced in a separate analysis), the most promising concept utilizes platoons both on the highway and on on-ramps. However, it is unclear whether comparable capacity can be achieved on exit, when vehicles must be decoupled from their platoons, and whether it is safe for vehicles to enter the highway in closely spaced platoons. The analytical evaluation indicates that entrance/exit spacing on the order of one per 2 km or closer would be required to support highways with total capacity on the order of 20,000 vehicles per hour. Most likely, this would be achieved most efficiently if separate dedicated entrances are provided for automated vehicles, to minimize weaving on manual lanes

Effects of Communication Delay and Kinematic Variation in Vehicle Platooning

Vehicle platoons are efficient, closely-spaced groups of robotically controlled vehicles which travel at high speeds down the road, similar to carts in a train. Within this thesis, a promising control algorithm for vehicle platooning is explored. The control algorithm was previously demonstrated in a sterile setting which significantly reduced the challenges facing full-scale implementation of platoons, most notably loss of shared data and imprecision within the data. As found within this work, transmission loss and imprecise position, velocity, and acceleration data significantly degraded the control algorithm\u27s performance. Vehicles in the platoon became more closely spaced, changed speeds more frequently, and expended far more energy than necessary. Introducing a measure of each following vehicle\u27s position with respect to the lead vehicle into the control algorithm noticeably reduced platoon contraction. Adjusting the control algorithm\u27s responsiveness based on what data was successfully received reduced the speed-variations by vehicles. Finally, using past behavior to predict the next acceleration reduced the energy used by each vehicle. Combining these modifications with a model of the proposed communication scheme shows platoons of up to 25 vehicles are feasible

Autonomous detection and anticipation of jam fronts from messages propagated by inter-vehicle communication

In this paper, a minimalist, completely distributed freeway traffic information system is introduced. It involves an autonomous, vehicle-based jam front detection, the information transmission via inter-vehicle communication, and the forecast of the spatial position of jam fronts by reconstructing the spatiotemporal traffic situation based on the transmitted information. The whole system is simulated with an integrated traffic simulator, that is based on a realistic microscopic traffic model for longitudinal movements and lane changes. The function of its communication module has been explicitly validated by comparing the simulation results with analytical calculations. By means of simulations, we show that the algorithms for a congestion-front recognition, message transmission, and processing predict reliably the existence and position of jam fronts for vehicle equipment rates as low as 3%. A reliable mode of operation already for small market penetrations is crucial for the successful introduction of inter-vehicle communication. The short-term prediction of jam fronts is not only useful for the driver, but is essential for enhancing road safety and road capacity by intelligent adaptive cruise control systems.Comment: Published in the Proceedings of the Annual Meeting of the Transportation Research Board 200

Road Design Criteria and Capacity Estimation Based on Autonomous Vehicles Performances. First Results from the European C-Roads Platform and A22 Motorway

Several European road operators and authorities joined the C-Roads Platform with the aim of harmonising the deployment activities of cooperative intelligent transport systems (C-ITS). C-ITS research is preliminary to future automated-driving vehicles. The current conventional highways were designed on traditional criteria and models specifically developed for traffic flows of manually guided vehicles. Thus, this article describes some new criteria for designing and monitoring road infrastructures on the basis of performance features of autonomous (or self-driving) vehicles.The new criteria have been adopted to perform an accurate conformity control of the A22 Brenner motorway, included in the C-Roads Platform, and also to ascertain whether in future it may be travelled by automated vehicles in safety conditions. Always in accordance with the technical and scientific insights required by the C-Roads Platform, a traffic model has been implemented to estimate how the A22 capacity increases compared to current values, by taking various percentages of automated or manual vehicles into consideration. The results given by theoretical models indicate that the highway will be able to be travelled by automated vehicles in safety conditions. On the other hand, the lane capacity is due to increase up to 2.5 times more than the current capacities, experimentally determined through traffic data collected from 4 highway sections by means of Drake's flow model