Analysis of Injury Mechanisms Within Lead-Vehicle Stopped Impacts: Implications for Autonomous Vehicle Behavior Design

2021 ◽  
Author(s):  
Lauren Eichaker ◽  
Rakshit Ramachandra ◽  
John Bolte
Keyword(s):  
Autonomous Vehicle ◽  
Injury Mechanisms ◽  
Behavior Design ◽  
Lead Vehicle

Analysis of Injury Mechanisms Within Lead-Vehicle Stopped Impacts: Implications for Autonomous Vehicle Behavior Design

2020 ◽  
Author(s):  
Lauren Eichaker ◽  
Rakshit Ramachandra ◽  
John Bolte
Keyword(s):  
Lower Extremity ◽  
Autonomous Vehicle ◽  
Health Care Expenditures ◽  
The Body ◽  
Steering Wheel ◽  
Lower Extremity Injuries ◽  
Body Regions ◽  
Vehicle Technologies ◽  
Extremity Injuries ◽  
Lead Vehicle

Abstract Lead vehicle stopped crashes are a top contributor to traffic and health care expenditures out of NHTSA’s 37 pre-crash scenarios. It is important to better understand how these crashes occur, so that evolving autonomous vehicle technologies may be tailored towards injury mitigation in crash-imminent scenarios. Additionally, as autonomous vehicle technologies increase in prevalence and usage, out of position seating and distracted driving behavior may also increase. In order to analyze injury patterns in real-world lead vehicle stopped crashes, the public portal of Crash Injury Research Engineering Network (CIREN) was surveyed for lead vehicle stopped impacts. The review found that, of all the body regions, the thorax and lower extremity body regions frequently sustained AIS 3+ injuries (P < 0.01). Additionally, the upper extremity frequently sustained AID 3+ injuries in some scenarios. Steering wheel contact (often times through a deployed air bag) was the source of 62% of the thorax injuries and the knee bolster was the source of 76% of the lower extremity injuries. Truck impacts, and complicated crashes accounted for over 50% of the cases in the cohort. Automated vehicle behaviors have the potential to augment passive and active safety systems to potentially decrease the occurrence of AIS 3+ injuries by improving a vehicle’s response to lead vehicle stopped, crash imminent scenarios.

Influence of Speed Forecasting on the Performance of Ecological Adaptive Cruise Control

2019 ◽  
Author(s):  
Eunjeong Hyeon ◽  
Youngki Kim ◽  
Niket Prakash ◽  
Anna G. Stefanopoulou
Keyword(s):  
Fuel Economy ◽  
Autonomous Vehicles ◽  
Adaptive Cruise Control ◽  
Autonomous Vehicle ◽  
Cruise Control ◽  
Prediction Horizon ◽  
The Future ◽  
V2v Communications ◽  
Urban Conditions ◽  
Lead Vehicle

Abstract In congested urban conditions, the fuel economy of a vehicle can be highly affected by traffic flow and particularly, the immediately preceding (lead) vehicle. Thus, estimating the future trajectories of the lead vehicle is essential to optimize the following vehicle’s maneuvers for its fuel economy. This paper investigates the influence of speed forecasting on the performance of an ecological adaptive cruise control (eco-ACC) strategy for connected autonomous vehicles. The real-time speed predictor proposed in [1] is applied to forecast the future speed profiles of the lead vehicle over a short prediction horizon. Under the assumption that vehicle-to-vehicle (V2V) communications are available, V2V information from multiple lead vehicles is utilized in the prediction process. Eco-ACC is formulated in a model predictive control (MPC) framework to control the connected autonomous vehicle. The influence of the state prediction to the performance of eco-ACC in terms of fuel economy and acceleration is evaluated with different number of connected vehicles.

Assessing Fuel Economy From Automated Driving: Influence of Preview and Velocity Constraints

2016 ◽  
Author(s):  
Niket Prakash ◽  
Gionata Cimini ◽  
Anna G. Stefanopoulou ◽  
Matthew J. Brusstar
Keyword(s):  
Fuel Economy ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Automated Driving ◽  
Test Procedures ◽  
Optimal Velocity ◽  
Prediction Horizon ◽  
Allowable Error ◽  
Velocity Constraints ◽  
Lead Vehicle

Constrained optimization control techniques with preview are designed in this paper to derive optimal velocity trajectories in longitudinal vehicle following mode, while ensuring that the gap from the lead vehicle is both safe and short enough to prevent cut-ins from other lanes. The lead vehicle associated with the Federal Test Procedures (FTP) [1] is used as an example of the achieved benefits with such controlled velocity trajectories of the following vehicle. Fuel Consumption (FC) is indirectly minimized by minimizing the accelerations and decelerations as the autonomous vehicle follows the hypothetical lead. Implementing the cost function in offline Dynamic Programming (DP) with full drive cycle preview showed up to a 17% increase in Fuel Economy (FE). Real time implementation with Model Predictive Control (MPC) showed improvements in FE, proportional to the prediction horizon. Specifically, 20s preview MPC was able to match the DP results. A minimum of 1.5s preview of the lead vehicle velocity with velocity tracking of the lead was required to obtain an increase in FE. The optimal velocity trajectory found from these algorithms exceeded the presently allowable error from standard drive cycles for FC testing. However, the trajectory was still safe and acceptable from the perspective of traffic flow. Based on our results, regulators need to consider relaxing the constant velocity error margins around the standard velocity trajectories dictated by the FTP to encourage FE increase in autonomous driving.

105: Renal Injury Mechanisms in Motor Vehicle Collisions: Analysis of the Crash Injury Research and Engineering Network (CIREN) Dataset

2007 ◽  
Vol 177 (4S) ◽  
pp. 37-37
Author(s):  
James K. Kuan ◽  
Robert Kaufman ◽  
Jonathan L. Wright ◽  
Charles Mock ◽  
Avery B. Nathens ◽  
...  
Keyword(s):  
Renal Injury ◽  
Motor Vehicle ◽  
Motor Vehicle Collisions ◽  
Vehicle Collisions ◽  
Injury Mechanisms ◽  
Injury Research

DO OUR BRAINS OPPPOSE TO AUTONOMOUS VEHICLE KILLINGS MORE THAN TO OTHER MORAL RISKS? AN fMRI INVESTIGATION

2020 ◽  
Vol 2020 ◽  
pp. 1203-1205
Author(s):  
JinHo Yun ◽  
◽  
Eun-Ju Lee ◽  
Bo-yong Park ◽  
Kyoungseob Byeon ◽  
...  
Keyword(s):  
Autonomous Vehicle
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