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Driver Assistance and Autonomous Driving features are becoming nearly ubiquitous in new vehicles. The intent of the Driver Assistant features is to assist the driver in making safer decisions. The intent of Autonomous Driving features is to execute vehicle maneuvers, without human intervention, in a safe manner. The overall goal of Driver Assistance and Autonomous Driving features is to reduce accidents, injuries, and deaths with a comforting driving experience. However, different drivers can react differently to advanced automated driving technology. It is therefore important to consider and improve the adaptability of these advances based on driver behavior. In this paper, a human-centric approach is adopted to provide an enriching driving experience. We perform data analysis of the naturalistic behavior of drivers when performing lane change maneuvers by extracting features from extensive Second Strategic Highway Research Program (SHRP2) data of over 5,400,000 data files.

The influence of driver modeling and drive cycle target speed trace modification on vehicle dynamics within energy consumption simulations is studied. EPA dynamometer speed error criteria and the SAE J2951 Drive Quality Evaluation for Chassis Dynamometer Testing standard are applied to simulation outputs as proposed components of simulation validation, providing guidelines for acceptable vehicle speed outputs and allowing comparison of simulation results to reported EPA dynamometer test statistics. The combined effect of driver model tuning and drive cycle interpolation methods is investigated for the UDDS, HwFET and US06 drive cycles, with EPA-specified linearly interpolated speed trace and a PI controller driver as a baseline result.

Pedal misapplication (PM) crashes, i.e., crashes caused by a driver pressing one pedal while intending to press another pedal, have historically been identified by searching unstructured crash narratives for keywords and verified via labor-intensive manual inspection. This study proposes an alternative method to identify PM crashes using event data recorders (EDRs). Since drivers in emergency braking situations are motivated to hit the brake hard, it follows that drivers in emergency braking situations that commit a PM would likewise hit the accelerator hard, likely harder than accelerator pedal application during normal driving. Thus, the time-series accelerator pedal position and the derived accelerator pedal application rate were used to isolate accelerator misapplications. Additional strategic filters were applied based on characteristics observed from previous PM analyses to reduce false positive PM identifications.

Vehicle headlamps and roadway lighting are the major sources of illumination at night. These sources affect contrast - defined as the luminance difference of an object from its background - which drives visibility at night. However, the combined effect of vehicle headlamps and intersection lighting on object contrast has not been reported previously. In this study, the interactive effects of vehicle headlamps and overhead lighting on object contrast were explored based on earlier work that examined drivers’ visibility under three intersection lighting designs (illuminated approach, illuminated box, and illuminated approach + box). The goals of this study were to: 1) quantify object luminance and contrast as a function of a vehicle’s headlamps and its distance to an intersection using the three lighting designs; and, 2) to assess whether contrast influences visual performance and perceived visibility in a highly dynamic intersection environment.

Vehicle rollovers are one of the more severe crash modes in the US - accounting for 32% of all passenger vehicle occupant fatalities annually. One design enhancement to help prevent rollovers is Electronic Stability Control (ESC) which can reduce loss of control and thus has great promise to enhance vehicle safety. The objectives of this research were (1) to estimate the effectiveness of ESC in reducing the number of rollover crashes and (2) to identify cases in which ESC did not prevent the rollover to potentially advance additional ESC development. All passenger vehicles and light trucks and vans that experienced a rollover from 2006 to 2015 in the National Automotive Sampling System Crashworthiness Database System (NASS/CDS) were analyzed. Each rollover was assigned a crash scenario based on the crash type, pre-crash maneuver, and pre-crash events.

This paper focuses on the analysis of an EcoRouting system with minimum and maximum number of conditional stops. The effect on energy consumption with the presence and absence of road-grade information along a route is also studied. An EcoRouting system has been developed that takes in map information and converts it to a graph of nodes containing route information such as speed limits, stop lights, stop signs and road grade. A variable acceleration rate synthesis methodology is also introduced in this paper that takes into consideration distance, acceleration, cruise speed and jerk rate as inputs to simulate driver behavior on a given route. A simulation study is conducted in the town of Blacksburg, Virginia, USA to analyze the effects of EcoRouting in different driving conditions and to examine the effects of road grade and stop lights on energy consumption.

The goal of this paper is to estimate near-side injury risk in vehicles with the best side impact performance in the U.S. New Car Assessment Program (NCAP). The longer-term goal is to predict the incidence of crashes and injury outcomes in the U.S. in a future fleet of the 2025-time frame after current active and passive safety countermeasures are fully implemented. Our assumption was that, by 2025, all new vehicles will have side impact passive safety performance equivalent to current U.S. NCAP five star ratings. The analysis was based on real-world crashes extracted from case years 2010-2015 in the National Automotive Sampling System / Crashworthiness Data System (NASS/CDS) in which front-row occupants of late-model vehicles (Model Year 2011+) were exposed to a near-side crash.

Objective: Opposite-direction crashes can be extremely severe because opposing vehicles often have high relative speeds. The most common opposite direction crash scenario occurs when a driver departs their lane driving over the centerline and impacts a vehicle traveling in the opposite direction. This cross-centerline crash mode accounts for only 4% of all non-junction non-interchange crashes but 25% of serious injury crashes of the same type. One potential solution to this problem is the Lane Departure Warning (LDW) system which can monitor the position of the vehicle and provide a warning to the driver if they detect the vehicle is moving out of the lane. The objective of this study was to determine the potential benefits of deploying LDW systems fleet-wide for avoidance of cross-centerline crashes. Methods: In order to estimate the potential benefits of LDW for reduction of cross-centerline crashes, a comprehensive crash simulation model was developed.

Road departures are one of the most deadly crash modes, accounting for nearly one third of all crash fatalities in the US. Lane departure warning (LDW) systems can warn the driver of the departure and lane departure prevention (LDP) systems can steer the vehicle back into the lane. One purpose of these systems is to reduce the quantity of road departure crashes. This paper presents a method to predict the maximum effectiveness of these systems. Thirty-nine (39) real world crashes from the National Automotive Sampling System (NASS) Crashworthiness Data System (CDS) database were reconstructed using pre-crash velocities downloaded for each case from the vehicle event data recorder (EDR). The pre-crash velocities were mapped onto the vehicle crash trajectory. The simulations assumed a warning was delivered when the lead tire crossed the lane line. Each case was simulated twice with driver reaction times of 0.38 s and 1.36 s after which time the driver began steering back toward the road.

One of the principal goals of modern vehicle control systems is to ensure passenger safety during dangerous maneuvers. Their effectiveness relies on providing appropriate parameter inputs. Tire-road contact forces are among the most important because they provide helpful information that could be used to mitigate vehicle instabilities. Unfortunately, measuring these forces requires expensive instrumentation and is not suitable for commercial vehicles. Thus, accurately estimating them is a crucial task. In this work, two estimation approaches are compared, an observer method and a neural network learning technique. Both predict the lateral and longitudinal tire-road contact forces. The observer approach takes into account system nonlinearities and estimates the stochastic states by using an extended Kalman filter technique to perform data fusion based on the popular bicycle model.

Tire modeling plays an important role in the development of an Active Vehicle Safety System. As part of a larger project that aims at developing an integrated chassis control system, this study investigates the performance of a 19” all-season tire on ice for a sport utility vehicle. A design of experiment has been formulated to quantify the effect of operational parameters, specifically: wheel slip, normal load, and inflation pressure on the tire tractive performance. The experimental work was conducted on the Terramechanics Rig in the Advanced Vehicle Dynamics Laboratory at Virginia Tech. The paper investigates an approach for the parameterization of the Dugoff tire model based on the experimental data collected. Compared to other models, this model is attractive in terms of its simplicity, low number of parameters, and easy implementation for real-time applications.

The effectiveness of Forward Collision Warning (FCW) or similar crash warning/mitigation systems is highly dependent on driver acceptance. If a FCW system delivers the warning too early, it may distract or annoy the driver and cause them to deactivate the system. In order to design a system activation threshold that more closely matches driver expectations, system designers must understand when drivers would normally apply the brake. One of the most widely used metrics to establish FCW threshold is Time to Collision (TTC). One limitation of TTC is that it assumes constant vehicle velocity. Enhanced Time to Collision (ETTC) is potentially a more accurate metric of perceived collision risk due to its consideration of vehicle acceleration. This paper compares and contrasts the distribution of ETTC and TTC at brake onset in normal car-following situations, and presents probability models of TTC and ETTC values at braking across a range of vehicle speeds.

There are over 4,500 fatal intersection crashes each year in the United States. Intersection Advanced Driver Assistance Systems (I-ADAS) are emerging active safety systems designed to detect an imminent intersection crash and either provide a warning or perform an automated evasive maneuver. The performance of an I-ADAS will depend on the ability of the onboard sensors to detect an imminent collision early enough for an I-ADAS to respond in a timely manner. One promising method for determining the earliest detection opportunity is through the reconstruction of real-world intersection crashes. After determining the earliest detection opportunity, the required sensor range, orientation, and field of view can then be determined through the simulation of these crashes as if the vehicles had been equipped with an I-ADAS.

Animal-vehicle collision (AVC) is a significant safety issue on American roads. Each year approximately 1.5 million AVCs occur in the U.S., the majority of them involving deer. The increasing use of cameras and radar on vehicles provides opportunities for prevention or mitigation of AVCs, particularly those involving deer or other large animals. Developers of such AVC avoidance/mitigation systems require information on the behavior of encountered animals, setting characteristics, and driver response in order to design effective countermeasures. As part of a larger study, naturalistic driving data were collected in high AVC incidence areas using 48 participant-owned vehicles equipped with data acquisition systems (DAS). Continuous driving data including forward video, location information, and vehicle kinematics were recorded. The respective 11TB dataset contains 35k trips covering 360K driving miles.

Lane departure warning (LDW) systems can detect an impending road departure and deliver an alert to allow the driver to steer back to the lane. LDW has great potential to reduce the number of road departure crashes, but the effectiveness is highly dependent upon driver acceptance. If the driver perceives there is little danger after receiving an alert, the driver may become annoyed and deactivate the system. Most current LDW systems rely heavily upon distance to lane boundary (DTLB) in the decision to deliver an alert. There is early evidence that in normal driving DTLB may be only one of a host of other cues which drivers use in lane keeping and in their perception of lane departure risk. A more effective threshold for LDW could potentially be delivered if there was a better understanding of this normal lane keeping behavior. The objective of this paper is to investigate the lane keeping behavior of drivers in normal driving.

Event data recorders (EDRs) must survive regulatory frontal and side compliance crash tests if installed within a car or light truck built on or after September 1, 2012. Although previous research has shown that EDR data are surviving these tests, little is known about whether EDRs are capable of surviving collisions of higher delta-v, or crashes involving vehicle fire or immersion. The goal of this study was to determine the survivability of light vehicle EDRs in real world fire, immersion, and high change in velocity (delta-v) cases. The specific objective was to identify the frequency of these extreme events and to determine the EDR data download outcome when subject to damage caused by these events. This study was performed using three crash databases: the Fatality Analysis Reporting System (FARS), the National Automotive Sampling System / Crashworthiness Data System (NASS/CDS), and the National Motor Vehicle Crash Causation Survey (NMVCCS).

Event data recorder (EDR) data are currently only required to survive the crash tests specified by Federal Motor Vehicle Safety Standard (FMVSS) 208 and FMVSS 214. Although these crash tests are severe, motor vehicles are also exposed to more severe crashes, fire, and submersion. Little is known about whether current EDR data are capable of surviving these events. The objective of this study was to determine the limits of survivability for EDR data for realistic car crash conditions involving heat, submersion, and static crush. Thirty-one (31) EDRs were assessed in this study: 4 in the pilot tests and 27 in the production tests. The production tests were conducted on model year (MY) 2011-2012 EDRs enclosed in plastic, metal, or a combination of both materials. Each enclosure type was exposed to 9 tests. The high temperature tests were divided into 3 oven testing conditions: 100°C, 150°C, and 200°C.

The Hybrid Electric Vehicle Team of Virginia Tech (HEVT) is participating in the 2012-2014 EcoCAR 2: Plugging in to the Future Advanced Vehicle Technology Competition series organized by Argonne National Lab (ANL), and sponsored by General Motors Corporation (GM) and the U.S. Department of Energy (DOE). The goals of the competition are to reduce well-to-wheel (WTW) petroleum energy consumption (PEU), WTW greenhouse gas (GHG) and criteria emissions while maintaining vehicle performance, consumer acceptability and safety. Following the EcoCAR 2 Vehicle Development Process (VDP), HEVT is designing, building, and refining an advanced technology vehicle over the course of the three year competition using a 2013 Chevrolet Malibu donated by GM as a base vehicle.

The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is ready to compete in the Year 3 Final Competition for EcoCAR 2: Plugging into the Future. The team is confident in the reliability of their vehicle, and expects to finish among the top schools at Final Competition. During Year 3, the team refined the vehicle while following the EcoCAR 2 Vehicle Development Process (VDP). Many refinements came about in Year 3 such as the implementation of a new rear subframe, the safety analysis of the high voltage (HV) bus, and the integration of Charge Sustaining (CS) control code. HEVT's vehicle architecture is an E85 Series-Parallel Plug-In Hybrid Electric Vehicle (PHEV), which has many strengths and weaknesses. The primary strength is the pure EV mode and Series mode, which extend the range of the vehicle and reduce Petroleum Energy Usage (PEU) and Greenhouse Gas (GHG) emissions.

A Location-Aware Adaptive Vehicle Dynamics System (LAAVDS) is developed to assist the driver in maintaining vehicle handling capabilities through various driving maneuvers. An integral part of this System is an Intervention Strategy that uses a novel measure of handling capability, the Performance Margin, to assess the need to intervene. Through this strategy, the driver's commands are modulated to affect desired changes to the Performance Margin in a manner that is minimally intrusive to the driver's control authority. Real-time implementation requires the development of computationally efficient predictive vehicle models. This work develops one means to alter the future vehicle states: modulating the driver's brake commands. This control strategy must be considered in relationship to changes in the throttle commands. Three key elements of this strategy are developed in this work.