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The original Table 2 includes incorrect data under the "Primary Energy" column for "multi-strike", "continuous" and "plasma" system types.

1. On page 111, the authors have described a method to assess driver distraction. In this method, participants maintained a white square size on a forward display by using a game gas pedal of like in car-following situation. The size of the white square is determined by calculating the distance to a virtual lead vehicle. The formulas to correct are used to explain variation of acceleration of the virtual lead vehicle. The authors inadvertently incorporated old formulas they had used previously. In the experiments discussed in the article, the corrected formulas were used. Therefore, there is no change in the results. The following from the article:

This is a errata for 2017-01-2520.

Remote diagnostic systems support diagnostic communication by having the capability of sending diagnostic request services to a vehicle and receiving diagnostic response services from a vehicle. These diagnostic services are specified in diagnostic protocols, such as SAE J1979, SAE J1939 or ISO 14229 (UDS). For the purpose of diagnostic communication, the tester needs access to the electronic control units as communication partners. Physically, the diagnostic tester gets access to the entire vehicle´s E/E system, which consists of connectors, wiring, the in-vehicle network (e.g. CAN), the electronic control units, sensors, and actuators. Any connection of external test equipment and the E/E system of a vehicle poses a security vulnerability. The combination can be used for malicious intrusion and manipulation.

Recent field data have shown that the occupant protection in vehicle rear seats failed to keep pace with advances in the front seats likely due to the lack of advanced safety technologies. The objective of this study was to optimize advanced restraint systems for protecting rear seat occupants with a range of body sizes under different frontal crash pulses. Three series of sled tests (baseline tests, advanced restraint trial tests, and final tests), MADYMO model validations against a subset of the sled tests, and design optimizations using the validated models were conducted to investigate rear seat occupant protection with 4 Anthropomorphic Test Devices (ATDs) and 2 crash pulses.

Engine cooling module air flows depend on package components and vehicle front end geometry. For years, in the early stages of vehicle development, front end geometry air flows were determined from 3/8 scale models or retrofit of similar existing vehicles. As time to market has become much shorter, finite element modeling of air flows is the only tool available. This paper describes how finite element simulations of front end air flows can be run early in the development program independent of any specific engine cooling module configuration and then coupled with traditional one-dimensional component performance models to predict cooling module air flows. The CFD simulation thus replaces the previous scale model testing process. The CFD simulations are used to determine the two parameters that characterize the front end geometry flow resistance (recovery coefficient and internal loss coefficient).

For over 30 years, field research and laboratory testing has consistently demonstrated that proper utilization of a seat belt dramatically reduces the risk of occupant death or serious injury in motor vehicle crashes. The injury prevention benefits of seat belts require that they remain fastened during collisions. Federal Motor Vehicle Safety Standards and SAE Recommended Practices set forth seat belt requirements to ensure proper buckle performance in accident conditions. Numerous analytical and laboratory studies have investigated buckle inertial release properties. Studies have repeatedly demonstrated that current buckle designs have inertial release thresholds well above those believed to occur in real-world crashes. Nevertheless, inertial release theories persist. Various conceptual amplification theories, coupled with high magnitude accelerations measured on vehicle frame components are used as support for these release theories.

An automated process was developed for the calibration of numerical aftertreatment models. The chemical kinetic mechanism examined in this case was part of a simplified SCR model. The process adopted for calibrating the SCR model was based on a micro-population multi-objective genetic algorithm. The algorithm developed was used to calibrate the SCR model using data derived from another, more detailed model to ensure that the evaluation focused only on the effectiveness of the calibration process and was not affected by issues of experimental inaccuracies or details of the model chemistry involved.

Four of these Particulate Reduction Systems (PMS) were tested on a passenger car and one of them on a HDV. Expectation of the research team was that they would reach at least a PM-reduction of 30% under all realistic operating conditions. The standard German filter test procedure for PMS was performed but moreover, the response to various operating conditions was tested including worst case situations. Besides the legislated CO, NOx and PM exhaust-gas emissions, also the particle count and NO2 were measured. The best filtration efficiency with one PMS was indeed 63%. However, under critical but realistic conditions filtration of 3 of 4 PMS was measured substantially lower than the expected 30 %, depending on operating conditions and prior history, and could even completely fail. Scatter between repeated cycles was very large and results were not reproducible. Even worse, with all 4 PMS deposited soot, stored in these systems during light load operation was intermittently blown-off.

Premixed Charge Compression Ignition (PCCI), a Low Temperature Combustion (LTC) strategy for diesel engines is of increasing interest due to its potential to simultaneously reduce soot and NOx emissions. However, the influence of mixture preparation on combustion phasing and heat release rate in LTC is not fully understood. In the present study, the influence of injection timing on mixture preparation, combustion and emissions in PCCI mode is investigated by experimental and computational methods. A sequential coupling approach of 3D CFD with a Stochastic Reactor Model (SRM) is used to simulate the PCCI engine. The SRM accounts for detailed chemical kinetics, convective heat transfer and turbulent micro-mixing. In this integrated approach, the temperature-equivalence ratio statistics obtained using KIVA 3V are mapped onto the stochastic particle ensemble used in the SRM.

The effect that thermally and compositionally stratified flowfields have on the spatial progression of iso-octane-fueled homogeneous charge compression ignition (HCCI) combustion were directly observed using highspeed chemiluminescence imaging. The stratified in-cylinder conditions were produced by independently feeding the intake valves of a four-valve engine with thermally and compositionally different mixtures of air, vaporized fuel, and argon. Results obtained under homogeneous conditions, acquired for comparison to stratified operation, showed a small natural progression of the combustion from the intake side to the exhaust side of the engine, a presumed result of natural thermal stratification created from heat transfer between the in-cylinder gases and the cylinder walls. Large differences in the spatial progression of the HCCI combustion were observed under stratified operating conditions.