Intersection Collision Avoidance System Research Articles

Investigating the Probability of Potential Actions Performed by Drivers Experiencing Daytime Sun Glare-Induced Blindness

There has been extensive visual-disability-focused safety research resulting from various factors/conditions that have a significant impact on a driver’s ability to maneuver safely. One such critical condition among the major environmental issues that contribute to traffic crashes is daytime sun glare-induced temporary visual impairment. Nevertheless, the contribution of relevant factors to the choice of potential actions at the time of such crashes has not been widely studied. Approximately 3,000 crashes per year in the United States are attributed to sun glare, which may indicate that the driver had no control over the situation. Thus, this study investigated several factors to identify crashes that occurred, with acceptable certainty, under the direct influence of sun glare over 4 years (2014 to 2017) in Florida—“the Sunshine State.” A multinomial logistic regression model was developed to formulate relationships between crash-related contributing factors and to evaluate the likelihood of alternative actions being adopted by drivers experiencing sun glare. The results indicated that running red lights/stop signs was the most likely action one might take while experiencing this adverse condition, particularly on local roadways. A further consequence of sun glare is that drivers tend to follow the car in front too closely along segments with higher annual average daily traffic. The findings could help safety officials support the use of emerging intelligent transportation system technologies (i.e., automated traffic signal performance measures and cooperative intersection collision avoidance systems) to alert drivers of the possibility of a temporary vision impairment exactly when high-glare conditions exist.

Rules Based Intersection Collision Avoidance System for V2X Safety

The biggest problem associated with the increased use of private and public transport is the increasing number of accidents on the roads. Most of the intersection collision avoidance systems use vehicle-to-infrastructure communication to avoid accidents at urban intersections. However, they are costly because additional roadside infrastructure must be installed, and they suffer from problem-related to real-time information delivery. The proposed system namely, Rules-based Intersection Collision Avoidance System (RICA) is a novel application using GPS technology for vehicle-to-everything (V2X) safety. The distance to the intersection is calculated and a time-to-intersection index is computed to establish the risk of a collision. The proposed system was achieved through simulations. It is potential as a new intersection collision avoidance system for V2X safety based on vehicular ad hoc network (VANET) communication. The result reveals that the proposed system can effectively prevent the occurrence of traffic crashes at an intersection.

Traffic Signal Phase and Timing Estimation From Low-Frequency Transit Bus Data

The objective of this paper is to demonstrate the feasibility of estimating traffic signal phase and timing from statistical patterns in low-frequency vehicular probe data. We use a public feed of bus location and velocity data in the city of San Francisco, CA, USA, as an example data source. We show that it is possible to estimate, fairly accurately, cycle times and the duration of reds for fixed-time traffic lights traversed by buses using a few days' worth of aggregated bus data. Furthermore, we also estimate the start of greens in real time by monitoring the movement of buses across intersections. The results are encouraging, given that each bus sends an update only sporadically ( $\approx$ every 200 m) and that bus passages are infrequent (every 5–10 min) . When made available on an open server, such information about the traffic signals' phase and timing can be valuable in enabling new fuel efficiency and safety functionalities in connected vehicles. Velocity advisory systems can use the estimated timing plan to calculate velocity trajectories that reduce idling time at red signals and therefore improve fuel efficiency and lower emissions. Advanced engine management strategies can shut down the engine in anticipation of a long idling interval at red. Intersection collision avoidance and active safety systems could also benefit from the prediction.

Supporting Heterogeneity in Cyber-Physical Systems Architectures

Cyber-physical systems (CPS) are heterogeneous, because they tightly couple computation, communication, and control along with physical dynamics, which are traditionally considered separately. Without a comprehensive modeling formalism, model-based development of CPS involves using a multitude of models in a variety of formalisms that capture various aspects of the system design, such as software design, networking design, physical models, and protocol design. Without a rigorous unifying framework, system integration and integration of the analysis results for various models remains ad hoc. In this paper, we propose a multi-view architecture framework that treats models as views of the underlying system structure and uses structural and semantic mappings to ensure consistency and enable system-level verification in a hierarchical and compositional manner. Throughout the paper, the theoretical concepts are illustrated using two examples: a quadrotor and an automotive intersection collision avoidance system.

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

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.

A Real-time Implementation of an Intersection Collision Avoidance System

AbstractThis paper presents a real-time implementation of a collision avoidance (CA) system that uses autonomous braking and model predictive control to assist drivers in avoiding collisions with other road users. To the authors knowledge, this is the first CA system that targets general vehicle collisions that has been implemented in a car. The system is based on a recently published decision-making algorithm which is described in [1]. To validate the CA system in various collision scenarios without endangering the driver of the vehicle, a novel test platform has been developed. The test platform consist of a soft crashable obstacle which is movable in speeds up to 70 km/h and safe to collide with in any angle in relative speeds up to 100 km/h. In the current implementation, estimates of the motion of the obstacle are obtained through a reference sensor fusion system that is based on a combination of in-vehicle sensors and a differential global positioning system. Results from both intersection and rear-end collision situations are presented. The results show that the proposed CA system can be implemented in a real-time environment and that the predictive brake control algorithm accurately accounts for delays and ramp-up times in the brake system of the vehicle.

Inexpensive, Infrastructure-Based, Intersection Collision-Avoidance System to Prevent Left-Turn Crashes with Opposite-Direction Traffic

An infrastructure-based intersection collision-avoidance system (ICAS) designed to provide guidance for left-turning vehicles that have secondary right-of-way and are attempting a turn across the path of opposite-direction traffic is proposed. These types of crashes are most prevalent where a left turn is made from a major road approach. A dominant causal factor in this type of crash is the inability of the left-turning motorist to adequately perceive the required gap for a safe left turn. The proposed ICAS compares the presumed arrival time of the opposite-direction vehicle with the presumed time for a motorist to turn left. Based on this comparison, the system provides guidance to the left-turning motorist in the form of a dynamic sign placed in the same line of sight of opposite-direction traffic, so that motorists will have a “second opinion” to aid in their left-turn decisions. The system is composed of four modules: ( a) a left-turn assistance device, ( b) left-turn presence detection, ( c) opposite-direction detection, and ( d) a system processor. The system differentiates between left-turning passenger cars and left-turning heavy vehicles and tailors its guidance appropriately. A non-hardware-specific example of the proposed system at a hypothetical intersection was assessed, along with functional requirements and system limitations.

Infrastructure Collision-Avoidance Concept for Straight-Crossing-Path Crashes at Signalized Intersections

Concepts to improve intersection safety with infrastructure-only and infrastructure-vehicle-cooperative collision-avoidance systems were assessed. These systems address straight-crossing-path crashes at signalized intersections. System components include sensors, processors, driver information devices, roadside-to-vehicle communication systems, and on-vehicle systems. The deployment model to realize infrastructure vehicle cooperation assumes the introduction of infrastructure-only systems that service all drivers with warnings provided by roadside information devices. These systems would be followed by the addition of roadside-to-vehicle communication systems that service drivers of intelligent vehicles through in-vehicle warnings. These concepts can be used to avoid a crash when a vehicle violates the signal. Equations of motion illustrate how a simple algorithm can be used to identify potential violators by using speed at a measurement point on the approach to an intersection. The example suggests that most potential signal violators can be warned in time to stop before they collide with a vehicle from the cross street. Analysis also indicates that when warnings of the possible signal violation are conveyed to drivers on the cross street, many of these drivers can also stop before colliding with the signal violator. In the example, 88% of the relevant straight-crossing-path crashes were addressed by providing timely warnings to either the violators or the other drivers. Deployment costs and benefits were estimated to demonstrate the potential value of infrastructure intersection collision-avoidance systems.

Brake Pulsing as Haptic Warning for an Intersection Collision Avoidance Countermeasure

Human factors issues involved in defining driver warnings for potentially hazardous situations at intersections are addressed. General issues include what information to present to drivers (warning content), when to present it (timing of warning), and how to present it (type of warning modality). The work reported is being conducted to assist in the design of a driver-vehicle interface (DVI) warning system for the Intersection Collision Avoidance Using Intelligent Vehicle Highway System Countermeasures (ICA) program. The ICA program is one of four collision avoidance system programs sponsored by the NHTSA Office of Crash Avoidance Research. Initially, the results of an on-road baseline study that examined driver behavior during approaches to intersections controlled by stop signs are reviewed with respect to the timing of driver warnings. Subsequently, haptic warning characteristics for an intersection collision avoidance system are defined, and DVI design guideline recommendations for implementing haptic warnings generated through vehicle brake pulsing are included. Vehicle brake system modifications required to achieve brake-pulsing parameters are described. Human factors test plans directed toward verification that physical properties generated through brake pulsing are readily recognized and accepted by drivers are delineated. As part of verification efforts, track tests will be performed with various intersection approach speeds and driving maneuvers. Effect of velocity on perception of haptic warning will be evaluated for ranges of magnitudes and duration of brake pulsing. The test results will support ICA test-bed development in the latter part of the ICA program.