Intersection management is one of the most challenging problems within the transport system. Traffic light-based methods have been efficient but are not able to deal with the growing mobility and social challenges. On the other hand, the advancements of automation and communications have enabled cooperative intersection management, where road users, infrastructure, and traffic control centers are able to communicate and coordinate the traffic safely and efficiently. Major techniques and solutions for cooperative intersections are surveyed in this paper for both signalized and nonsignalized intersections, whereas focuses are put on the latter. Cooperative methods, including time slots and space reservation, trajectory planning, and virtual traffic lights, are discussed in detail. Vehicle collision warning and avoidance methods are discussed to deal with uncertainties. Concerning vulnerable road users, pedestrian collision avoidance methods are discussed. In addition, an introduction to major projects related to cooperative intersection management is presented. A further discussion of the presented works is given with highlights of future research topics. This paper serves as a comprehensive survey of the field, aiming at stimulating new methods and accelerating the advancement of automated and cooperative intersections.

Cooperative Intersection Management: A Survey

The ability to classify driver behavior lays the foundation for more advanced driver assistance systems. In particular, improving safety at intersections has been identified as a high priority due to the large number of intersection-related fatalities. This paper focuses on developing algorithms for estimating driver behavior at road intersections and validating them on real traffic data. It introduces two classes of algorithms that can classify drivers as compliant or violating. They are based on (1) support vector machines and (2) hidden Markov models, which are two very popular machine learning approaches that have been used successfully for classification in multiple disciplines. However, existing work has not explored the benefits of applying these techniques to the problem of driver behavior classification at intersections. The developed algorithms are successfully validated using naturalistic intersection data collected in Christiansburg, VA, through the U.S. Department of Transportation Cooperative Intersection Collision Avoidance System for Violations initiative. Their performances are also compared with those of three traditional methods, and the results show significant improvements with the new algorithms.

Driver Behavior Classification at Intersections and Validation on Large Naturalistic Data Set

We introduce zonotope bundles for computing the set of states reachable by a dynamical system, also known as the reachable set. Reachable set computations suffer from the curse of dimensionality, which has been successfully addressed by using zonotopes for linear systems. However, zonotopes are not closed under intersection leading to challenges when applying them to nonlinear and hybrid problems. We introduce zonotope bundles as the intersection of zonotopes (without explicitly computing the intersection). Zonotope bundles are closed under intersection, while inheriting many positive properties of zonotopes. This is demonstrated for linear, nonlinear, and hybrid systems. A further property of zonotope bundles is that their computation can be easily parallelized.

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Zonotope bundles for the efficient computation of reachable sets

The ability to classify driver behavior lays the foundation for more advanced driver assistance systems. Improving safety at intersections has also been identified as high priority due to the large number of intersection related fatalities. This paper focuses on developing algorithms for estimating driver behavior at road intersections. It introduces two classes of algorithms that can classify drivers as compliant or violating. They are based on 1) Support Vector Machines (SVM) and 2) Hidden Markov Models (HMM), two very popular machine learning approaches that have been used extensively for classification in multiple disciplines. The algorithms are successfully validated using naturalistic intersection data collected in Christiansburg, VA, through the US Department of Transportation Cooperative Intersection Collision Avoidance System for Violations (CICAS-V) initiative.

/pdf/behavior-classification-algorithms-at-intersections-and-97vf2twbvi.pdf

Behavior classification algorithms at intersections and validation using naturalistic data

A safety system determines a correspondence between a vehicle and an upcoming traffic signal, and predicts a state of the traffic signal when the vehicle is expected to reach the signal's location. Based on proximity to the signal and speed of the vehicle, the system provides a warning if it determines that the vehicle is likely to proceed contrary to the signal (e.g., a driver is likely to run a red light). The warning is issued to the vehicle about to illegally enter the intersection, to other nearby vehicles and pedestrians, and to the traffic signal itself.

Driver Safety Enhancement Using Intelligent Traffic Signals and GPS

Intersection crashes account for 1.72 million crashes per year in the United States. In 2004 stop-sign and traffic signal violations accounted for approximately 302,000 crashes resulting in 163,000 functional life-years lost and $7.9 billion of economic loss. The objective of the Cooperative Intersection Collision Avoidance System for Violations (CICAS-V) project was to design, develop, and test a prototype system to prevent crashes by predicting stop-sign and signal-controlled intersection violations and warning the violating driver. The intersection portion of the system consists of a signal controller capable of exporting signal phase and timing information, a local global positioning system (GPS), and Roadside Equipment (RSE) that includes computing, memory, and Dedicated Short Range Communication (DSRC) radio. The vehicle portion of the system includes onboard equipment for computing and 5.9 GHz DSRC radio connected to the vehicle controller area network (CAN), positioning, and the Driver-Vehicle Interface (DVI). The intersection sends the signal phase and timing, positioning corrections, and a small map (< 1 kb) to the vehicle. The vehicle receives this information and, based on speed and distance to the stop location, predicts whether or not the driver will violate. If a violation is predicted, the driver is warned via a visual/auditory/haptic brake pulse DVI. The system was installed in the vehicles of five Original Equipment Manufacturers (OEMs): Daimler, Ford, General Motors, Honda, and Toyota. Intersections were equipped in California, Michigan, and Virginia. Tests of the system included both on-road and test-track evaluations. System performance was excellent and recommendations were made for continuing with a large field operational test (FOT). The system can be installed at any intersection with sufficient positioning coverage and in any vehicle with an electronic stability system. This system constitutes the first FOT-ready Vehicle Infrastructure Integration safety application. The full text of this paper may be found at: http://www-nrd.nhtsa.dot.gov/pdf/esv/esv21/09-0118.pdf For the covering abstract see ITRD E145407.

Cooperative Intersection Collision Avoidance System for Violations (CICAS-V) for Prevention of Violation-Based Intersection Crashes

A system, for assisting a driver of a vehicle 16 when reaching an intersection 10, comprises a detection module which detects a position, orientation, and motion state of at least one other traffic participant 17 approaching or at the intersection by means of at least one sensor. A localization module uses the detected information to compute a distance of the vehicles to their applicable stop position at the intersection 10. A situation analysis module combines the information from the detection module and the localization module to compute estimated arrival times, performs a precedence evaluation, and evaluates a probability for the vehicle 16 to have the right of way at the intersection depending on times at which the vehicle 16 and the other traffic participant 17 reach their respective stop positions. A communication module warns the driver if the driver is likely to run the stop position and does not have the right of way depending on the evaluated probability. The communication module may apply a brake and/or change gas pedal resistance. The detection module may detect at least one traffic control device, e.g. stop signs.

Michael Maile

Papers

Cooperative Intersection Collision Avoidance System for Violations (CICAS-V) for Prevention of Violation-Based Intersection Crashes

Driver assistance system