Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models

Advanced Driver Assistance Systems (ADAS) have made driving safer over the last decade. They prepare vehicles for unsafe road conditions and alert drivers if they perform a dangerous maneuver. However, many accidents are unavoidable because by the time drivers are alerted, it is already too late. Anticipating maneuvers beforehand can alert drivers before they perform the maneuver and also give ADAS more time to avoid or prepare for the danger. In this work we anticipate driving maneuvers a few seconds before they occur. For this purpose we equip a car with cameras and a computing device to capture the driving context from both inside and outside of the car. We propose an Autoregressive Input-Output HMM to model the contextual information alongwith the maneuvers. We evaluate our approach on a diverse data set with 1180 miles of natural freeway and city driving and show that we can anticipate maneuvers 3.5 seconds before they occur with over 80\% F1-score in real-time.Comment: ICCV 2015, http://brain4cars.co

Tracking Driver Eye Movements at Permissive Left-Turns

The objective of this analysis was to identify sources of informationused by left-turning drivers. To complete the experiment, a virtual network ofsignalized intersections was created for use in a driving simulator equipped withhead and eye tracking equipment. Fourteen drivers were recruited to participate inthe experiment, which included two independent variables (permissive signalindication and presence of opposing traffic). The primary dependent variable wasthe associated eye movements at permissive left-turns, including the magnitude oftime focused on each potential cue and the pattern in which cues were detected.To complete the analysis, eye movements were tracked and the screen wasdivided into “areas of interest,” which coincided with potential cues used in thecompletion of a permissive left turn. For each permissive scenario, drivers usedmore total cues when no opposing traffic was present. Specifically, in theabsence of opposing traffic, drivers fixated on a wider array of availableinformation. When opposing traffic was present, drivers spent a majority of timefocused on opposing traffic and would use this as a base point from which theywould glance at other data sources. Overall, drivers looked at least once at theprotected/permissive left-turn (PPLT) signal display and the opposing trafficstream. Drivers tended to scan the intersection from right to left, after initiallylocating the PPLT signal display and opposing traffic and/or stop bar area. Theresults of the eye movement analysis are consistent with data obtained in afollow-up static evaluation

New Analytic Solutions of Queueing System for Shared-Short Lanes at Unsignalized Intersections

Designing the crossroads capacity is a prerequisite for achieving a high level of service with the same sustainability in stochastic traffic flow. Also, modeling of crossroad capacity can influence on balancing (symmetry) of traffic flow. Loss of priority in a left turn and optimal dimensioning of shared-short line is one of the permanent problems at intersections. A shared-short lane for taking a left turn from a priority direction at unsignalized intersections with a homogenous traffic flow and heterogeneous demands is a two-phase queueing system requiring a first in-first out (FIFO) service discipline and single-server service facility. The first phase (short lane) of the system is the queueing system M(p lambda)/M(mu)/1/infinity, whereas the second phase (shared lane) is a system with a binomial distribution service. In this research, we explicitly derive the probability of the state of a queueing system with a short lane of a finite capacity for taking a left turn and shared lane of infinite capacity. The presented formulas are under the presumption that the system is Markovian, i.e., the vehicle arrivals in both the minor and major streams are distributed according to the Poisson law, and that the service of the vehicles is exponentially distributed. Complex recursive operations in the two-phase queueing system are explained and solved in manuscript

ORT12A Study on Sight Distance at Urban Uncontrolled Intersections: A Case Study in Bauchi State-Nigeria

The safe operation at intersection or driveways requires adequate sight distance so drivers can enter the roadway safely. Sight distance is the length of roadway visible to the driver. This study compares standards and current practice on intersection sight distance within Bauchi metropolis for a four-way and ‘T’ intersections respectively. Primary emphasis being on the traffic operation conditions, drivers’ behavior and vehicle operation characteristics that influence the required intersection sight distance. Keywords: intersection, sight distance, sight triangle, closure

Situation-Aware Left-Turning Connected and Automated Vehicle Operation at Signalized Intersections

One challenging aspect of the Connected and Automated Vehicle (CAV) operation in mixed traffic is the development of a situation-awareness module for CAVs. While operating on public roads, CAVs need to assess their surroundings, especially the intentions of non-CAVs. Generally, CAVs demonstrate a defensive driving behavior, and CAVs expect other non-autonomous entities on the road will follow the traffic rules or common driving behavior. However, the presence of aggressive human drivers in the surrounding environment, who may not follow traffic rules and behave abruptly, can lead to serious safety consequences. In this paper, we have addressed the CAV and non-CAV interaction by evaluating a situation-awareness module for left-turning CAV operations in an urban area. Existing literature does not consider the intent of the following vehicle for a CAVs left-turning movement, and existing CAV controllers do not assess the following non-CAVs intents. Based on our simulation study, the situation-aware CAV controller module reduces up to 27% of the abrupt braking of the following non-CAVs for scenarios with different opposing through movement compared to the base scenario with the autonomous vehicle, without considering the following vehicles intent. The analysis shows that the average travel time reductions for the opposite through traffic volumes of 600, 800, and 1000 vehicle/hour/lane are 58%, 52%, and 62%, respectively, for the aggressive human driver following the CAV if the following vehicles intent is considered by a CAV in making a left turn at an intersection

Driver Understanding of the Flashing Yellow Arrow and Dynamic No Turn on Red Sign for Right Turn Applications

Since their introduction to the 2009 Edition of the Manual on Uniform Traffic Control Devices, flashing yellow arrows (FYA) have had significant success in communicating the permissive turn message. While widely used for the permissive left turn maneuver, agencies recently have been utilizing flashing yellow arrows for the use with right turn applications as drivers interact with crossing pedestrians. As pedestrian conflicts are a concern during the permissive green phase, there is additional worry for the potential interaction between a pedestrian and vehicle turning right on red. This research explores the existing driver comprehension of permissive right turns during both green and red phases through static evaluation and microsimulation. Proposed traffic devices including the FYA and the Dynamic No Turn on Red sign were evaluated in relation to the existing signal and sign conditions implemented in the field. In comparing the proposed FYA to the existing circular green signal, the survey evaluation determined a statistically significant increase in drivers’ yielding responses when interacting with the FYA as opposed to the circular green. Through application of the VISSIM program, it was determined that right turning speeds with the FYA present were significantly lower than when interacting with solely the circular green. Both the static evaluation and microsimulation determined a strong similarity between the existing circular red and R10-11 sign and the proposed dynamic no turn on red sign which verifies the strong understanding drivers have of the message and the sign itself

Object level footprint uncertainty quantification in infrastructure based sensing

We examine the problem of estimating footprint uncertainty of objects imaged using the infrastructure based camera sensing. A closed form relationship is established between the ground coordinates and the sources of the camera errors. Using the error propagation equation, the covariance of a given ground coordinate can be measured as a function of the camera errors. The uncertainty of the footprint of the bounding box can then be given as the function of all the extreme points of the object footprint. In order to calculate the uncertainty of a ground point, the typical error sizes of the error sources are required. We present a method of estimating the typical error sizes from an experiment using a static, high-precision LiDAR as the ground truth. Finally, we present a simulated case study of uncertainty quantification from infrastructure based camera in CARLA to provide a sense of how the uncertainty changes across a left turn maneuver.Comment: Submitted to IEEE Sensors journa