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A step-ratio automatic transmission alters torque paths for gearshifting through engagement and disengagement of clutches. It enables torque sources to run efficiently while meeting driver demand. Yet, clutch thermal energy during gearshifting is one of the contributors to the overall fuel loss. In order to optimize drivetrain control strategy, including the frequency of shifts, it is important to understand the cost of shift itself. In a power-on upshift, clutch thermal energy is primarily dissipated during inertia phase. The interaction between multiple clutches, coupled with input torque truncation, makes the decomposition of overall energy loss less obvious. This paper systematically presents the mathematical analysis of clutch thermal energy during the inertia phase of a typical single-transition gearshift. In practice, a quicker shift is generally favored, partly because the amount of energy loss is considered smaller.

The paper deals with the design of shift scheduling maps based on dynamic programing (DP) optimization algorithm. The recorded data related to a delivery vehicle fleet are used, along with a model of delivery truck equipped with a 12-gear automated manual transmission, for an analysis and reconstruction of the truck-implemented shift scheduling patterns. The same map reconstruction procedure has been applied to a set of DP optimization-based operating points. The cost function of DP optimization is extended by realistic clutch energy losses dissipated during shift transients, in order to implicitly introduce hysteresis in the shift scheduling maps for improved drivability. The different reconstructed shift scheduling maps are incorporated within the truck model and validated by computer simulations for different driving cycles.

A detailed experimental validation has been carried out to point to limitations of static wet-clutch friction model for typical clutch engagement transients. The model accuracy can be increased by incorporating the fluid film dynamics, as done in the lumped-parameter dynamic clutch model developed at the University of Purdue. That model is extended herein in order to increase its accuracy especially in the case of grooved clutches. The extensions include a description of clutch actuator dynamics and introduction of an empirical scaling factor for the fluid film thickness state equation. More rigorous treatment of fluid dynamics for the grooved clutch is also presented.

The improvement of road transport safety requires the development of advanced vehicle safety systems, whose development could be facilitated by using complex interaction models of different road users. To this end, this paper deals with the modeling of multi-vehicle/multi-pedestrian interactions at unsignalized crosswalks. This multi-agent modeling approach extends on the existing basic model covering only single-vehicle/single-pedestrian interactions. The basic model structure and parameters have remained the same, as it was previously experimentally calibrated and thoroughly verified. The proposed modeling procedure employs the basic model within the multi-agent setting based on its application to relevant single-vehicle and single-pedestrian pairs. The resulting, so-called pre-decisions are then used for making final crossing decisions in a current time step for each agent.

To enable smooth and low-risk autonomous driving in the presence of other road users, such as cyclists and pedestrians, appropriate predictive safe speed control strategies relying on accurate and robust prediction models should be employed. However, difficulties related to driving scene understanding and a wide variety of features influencing decisions of other road users significantly complexifies prediction tasks and related controls. This paper proposes a hierarchical neural network (NN)-based prediction model of pedestrian crossing behavior, which is aimed to be applied within an autonomous vehicle (AV) safe speed control strategy. Additionally, different single-level prediction models are presented and analyzed as well, to serve as baseline approaches.

This paper proposes a method for multi-objective parameter optimization of piecewise linear time profiles for control of Automatic Transmission (AT) shifts and presents results obtained on an example of a powertrain with a 10-speed automatic transmission. The paper first outlines the powertrain dynamics model. Then, the AT control trajectory optimization approach is outlined and employed with the aim of getting insights into the optimal shift control profiles and related performance. The parameter optimization problem is to find parameters of piecewise linear shift control profiles, which provide a trade-off between the shift comfort and performance. The optimization problem is solved by using the multi-objective genetic algorithm MOGA-II incorporated within modeFRONTIER environment.

This paper considers using linear quadratic regulation (LQR) for multi-input control of the Automatic Transmission (AT) upshift inertia phase. The considered control inputs include the transmission input/engine torque, oncoming clutch torque, and traditionally not used off-going clutch torque. Use of the off-going clutch has been motivated by discussed Control Trajectory Optimization (CTO) results demonstrating that employing the off-going clutch during the inertia phase along with the main, oncoming clutch can improve the upshift control performance in terms of the shift duration and/or comfort by trading off the transmission efficiency and control simplicity to some extent. The proposed LQR approach provides setting an optimal trade-off between the conflicting criteria related to driving comfort and clutches thermal energy loss.

Automatic transmission (AT) upshift control performance in terms of shift duration and comfort can be improved during the inertia phase by coordinating the off-going clutch together with oncoming clutch and engine torque. The performance improvement is highest in low gear shifts (i.e., for high ratio steps), which are typically performed with open torque converter. In this paper, a discrete-time, linear quadratic regulation (LQR) is applied during the upshift inertia phase, as it provides an optimal multi-input/multi-output control action with respect to the prescribed cost function. The LQR law is based on a reduced-order drivetrain model, which is applicable to actual transmissions characterized by a limited number of available state measurements. The reduced-order model includes the linearized torque converter model. The shift duration is ensured by precise tracking of a linear-like oncoming clutch slip speed reference profile.