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Advanced Driver Assistance System (ADAS) has become a common standard feature assisting greater safety and fuel efficiency in the latest automobiles. Yet some ADAS systems fail to improve driving comfort for vehicle occupants who expect human-like driving. One of the more difficult situations in ADAS-assisted driving involves instances with cut-in vehicles. In vehicle control, determining the moment at which the system recognizes a cut-in vehicle as an active target is a challenging task. A well-designed comprehensive threat assessment developed for cut-in vehicle driving scenarios should eliminate abrupt and excessive deceleration of the vehicle and produce a smooth and safe driving experience. This paper proposes a novel methodology for threat assessment for driving instances involving a cut-in vehicle. The methodology takes into consideration kinematics, vehicle dynamics, vehicle stability, road condition, and driving comfort.

The rapid development of driver assistance systems, such as lane-departure warning (LDW) and lane-keeping support (LKS), along with widely publicized reports of automated vehicle testing, have created the expectation for an increasing amount of vehicle automation in the near future. As these systems are being phased in, the coexistence of automated vehicles and human-driven vehicles on roadways will be inevitable and necessary. In order to develop automated vehicles that integrate well with those that are operated in traditional ways, an appropriate understanding of human driver behavior in normal traffic situations would be beneficial. Unlike many research studies that have focused on collision-avoidance maneuvering, this paper analyzes the behavior of human drivers in response to cut-in vehicles moving at similar speeds. Both automated and human-driven vehicles are likely to encounter this scenario in daily highway driving.

In this paper, we focus on the active vehicular suspension controller design. A quarter-vehicle suspension system is employed in the system analysis and synthesis. Due to the difficulty and cost in the measuring of all the states, we only choose two variables to construct the feedback loop, that is, the control law is a static-output-feedback (SOF) control. However, the sensor reduction would induce challenges in the controller design. One of the main challenges is the NP-hard problem in the corresponding SOF controller design. In order to deal with this challenge, we propose a two-stage design method in which a state-feedback controller is firstly designed and then the state-feedback controller is used to decouple the nonlinear conditions. To better compensate for the varying vehicle load, a robust load-dependent control strategy is adopted. The proposed design methodology is applied to a suspension control example.

This paper describes a diesel engine lean NOx trap (LNT) regeneration air to fuel ratio (AFR) control system using a nonlinear model predictive control (NMPC) technique for simultaneous regeneration fuel penalty and overall tailpipe-out NOx reductions. A physics-based and experimentally validated nonlinear LNT dynamic model was employed to construct the NMPC control algorithm, which dictates the AFR value during regenerations. Different choices of NMPC cost function were examined in terms of the impact on fuel penalty and total tailpipe NOx slip amount. The cost function to achieve the best tradeoff between fuel penalty and tailpipe-out NOx was selected based on physical insights into the LNT system NOx and oxygen storage dynamics. The NMPC regeneration AFR control system was evaluated on a vehicle simulator cX-Emissions1 with a 1.9L diesel engine model through the FTP75 driving cycle.

Alternative combustion modes such as homogeneous charge compression ignition (HCCI) and low temperature combustion (LTC) have shown very promising engine-out emissions. However, these combustion modes are close to the combustion stability boundaries and very sensitive to in-cylinder condition variations. Thus, transient control of engines operating in alternative combustion modes are very challenging compared with control of engines with conventional combustion mode only. This paper presents some advanced air-path control and estimation techniques/practice for multiple and alternative combustion mode engine transient operation. Based on the mean-value engine dynamic models, robust controllers can be designed to track the desired air-path performance variables to ensure desirable combustion during transient operation.

This paper describes an in-cylinder lean-rich combustion (no-post-injection for rich) switching control approach for modern diesel engines equipped with exhaust-treatment systems. No-post-injection rich combustion is desirable for regeneration of engine exhaust-treatment systems thanks to its less fuel penalty compared with regeneration approaches using post-injections and / or in-exhaust injections. However, for vehicle applications, it is desirable to have driver-transparent exhaust-treatment system regenerations, which challenge the in-cylinder rich-lean combustion transitions. In this paper, a nonlinear in-cylinder condition control system combined with in-cylinder condition guided fueling control functions were developed to achieve smooth in-cylinder lean-rich switching control at both steady-state and transient operation. The performance of the control system is evaluated on a modern light-duty diesel engine (G9T600).

This paper describes a hybrid robust nonlinear control approach for modern diesel engines running low temperature combustion and conventional diesel combustion modes. Using alternative combustion modes has become a promising approach to reduce engine emissions. However, due to very different in-cylinder conditions and fueling parameters for different combustion modes, control of engines operating multiple combustion modes is very challenging. It becomes difficult for conventional calibration / mapping based approaches to produce satisfactory results in terms of engine torque responses and emissions. Advanced control techniques are then demanded to accomplish the tasks. An innovative hybrid control system is designed to track different key engine operating variables at different combustion modes as well as avoid singularity which is inherent for turbocharged diesel engines running multiple combustion modes.

This paper describes algorithms that can recognize and track the engine crankshaft position for arbitrary cam- and crank-shaft tooth wheel patterns in both steady-state and transient operating conditions. Crankshaft position tracking resolution is adjustable to accommodate different application requirements. The instantaneous crankshaft position information provided by the position tracking module form the basis for crankshaft angle domain (CAD) engine control and measurement functions such as precise injection / ignition controls and on-line cylinder pressure CAD analyses. The algorithms described make reconfiguration of the tracking module for different and arbitrary cam- and crank-shaft tooth wheel patterns very easy, which is valuable especially for prototyping engine control systems. The effectiveness of the algorithms is shown using test engines with different cam and crank signal patterns.

This paper investigates the effect of real-time control system computational delay on the performance of a hybrid adaptive cruise control (ACC) system during braking/coasting scenarios. A hierarchical hybrid ACC system with a finite state machine (FSM) at the high-rank and a nonlinear sliding mode controller (SMC) at the low-rank is designed based on a vehicle dynamics model with a brake-by-wire platform. From simulations, parametric studies are used to evaluate the effect of the bounded random computational delay on the system performance in terms of tracking errors and control effort. The effect of the computational delay location within the control system hierarchy is also evaluated. The system performance generally becomes worse as the upper boundary of the computational delay increases while the effect of the computational delay located at the high-rank controller is more pronounced.

This paper explores the possibility of on-board fuel classification for fuel property adaptive compression-ignition engine control system. The fuel classifier is designed to on-board classify the fuel that a diesel engine is running, including alternative and renewable fuels such as bio-diesel. Based on this classification, the key fuel properties are provided to the engine control system for optimal control of in-cylinder combustion and exhaust treatment system management with respect to the fuel. The fuel classifier employs engine input-output response characteristics measured from standard engine sensors to classify the fuel. For proof-of-concept purposes, engine input-output responses were measured for three different fuels at three different engine operating conditions. Two neural-network-based fuel classifiers were developed for different classification scenarios. Of the three engine operating conditions tested, two conditions were selected for the fuel classifier to be active.

Tremendous amount of useful information can be extracted from the cylinder pressure signal for engine combustion control. However, the physical cylinder pressure sensors are undesirably expensive and their health need to be monitored for fault diagnostic purpose as well. This paper presents the results of the development of a virtual cylinder pressure sensor (VCPS) with individual variable-oriented independent estimators. Two neural network-based independent cylinder pressure related variable estimators were developed and verified at steady state. The results show that these models can predict the variables correctly compared with the extracted variables from the measured physical cylinder pressure sensor signal. Good generalization capabilities of the developed models are observed in the sense that the models work well not only for the training data set but also for the new inputs that they have never been exposed to before.