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An obstacle recognition algorithm for the Pre-Crash Safety system has been newly developed with a stereo vision system and a millimeter wave radar with additional functions. This algorithm uses the merits of both the millimeter wave radar and the stereo vision system, and has two main features. One feature utilizes the merits of the stereo vision system detection with the detection results from the millimeter wave radar allowing for a more detailed horizontal position and width of the obstacle. This enables the equipment to operate at an earlier stage according to how well the relationship between the vehicle and the obstacle is understood. Another feature fuses detection from the millimeter wave radar and the stereo vision system. This system has succeeded in enhancing the detection performance of pedestrians who have been more difficult to detect than reflective objects such as cars.

The purpose of this study is to propose braking characteristics that are easy for drivers to handle in a system in which braking and driving operations are performed by hand. Genetic algorithm optimization of braking characteristics showed that the best deceleration tracking was achieved by an FG diagram with a logarithmic function shape. In contrast, the slope of the optimal FG diagram tended to decrease as the driver's proportional gain increased.

Toyota Safety Sense is a safety system package developed to help drivers avoid accident types with a high frequency of occurrence. This paper deals with pre-collision system which forms the core of Toyota Safety Sense, especially Toyota Safety Sense P which uses a combined sensor configuration consisting of a monocular camera paired with millimeter wave radar, in order to achieve both high recognition performance and reliability. The use of a wide-angle monocular camera, millimeter wave radar integrated in the front grill emblem, and a collision determination algorithm for pedestrian targets enabled the development of a pre-collision system comprising detection capability of crossing pedestrians. Toyota has developed warning and pre-collision brake assist for driver to assist in avoiding a collision effectively; In addition, Pre-collision brake has achieved high level of performance for the drivers who cannot avoid a collision.

Many vehicles are currently equipped with active safety systems that can detect vulnerable road users like pedestrians and bicyclists, to mitigate associated conflicts with vehicles. With the advancements in technologies and algorithms, detailed motions of these targets, especially the limb motions, are being considered for improving the efficiency and reliability of object detection. Thus, it becomes important to understand these limb motions to support the design and evaluation of many vehicular safety systems. However in current literature, there is no agreement being reached on whether or not and how often these limbs move, especially at the most critical moments for potential crashes. In this study, a total of 832 pedestrian walking or cyclist biking cases were randomly selected from one large-scale naturalistic driving database containing 480,000 video segments with a total size of 94TB, and then the 832 video clips were analyzed focusing on their limb motions.

A vehicle parking manoeuvre is characterized by low or zero speed, small turning radius and large yaw velocity of the steered wheels. To predict the forces and moments generated by a wheel under these conditions, the Pacejka Magic Formula model has been extended to incorporate the effect of spin (turn slip model) in the past years. The extensions have been further developed and incorporated in the MFTyre/MF-Swift 6.2 model. This paper describes the development of a method for the identification of the turn slip parameters. Based on the operating conditions of a typical parking manoeuvre, the dominant parameters of the turn slip model are firstly defined. At an indoor test facility, the response of a tyre under the identified operating conditions is measured. An algorithm is developed to identify the dominant turn slip parameters from the measured responses.

A few reports suggest differences in injury outcomes between cadaver tests and real-world accidents under almost similar conditions. This study hypothesized that muscle activity could primarily cause the differences, and then developed a human body finite element (FE) model with individual muscles. Each muscle was modeled as a hybrid model of bar elements with active properties and solid elements with passive properties. The model without muscle activation was firstly validated against five series of cadaver test data on impact responses in the anterior-posterior direction. The model with muscle activation levels estimated based on electromyography (EMG) data was secondly validated against four series of volunteer test data on bracing effects for stiffness and thickness of an upper arm muscle, and braced driver's responses under a static environment and a brake deceleration.

This paper gives an outline of the steering assistance system (hereinafter, SAS) and a description of its key technology: the lane recognition algorithm. To accommodate a variety of driving styles, the SAS is equipped with a lane keeping assistance mode (LKA mode) and a lane departure warning mode (LDW mode) that can be selectively set by the driver. The former mode works in combination with adaptive cruise control (ACC) and carries the advantage of relieving the driving load that is placed on the driver. The latter mode has the benefit of reducing the danger of lane departure accidents caused by the driver dozing off and taking his eyes off the road. The newly developed lane recognition ECU has a simple hardware set-up of two 32-bit microcomputers. The lane recognition algorithm was constructed on the basis of a logic process that analyzes pattern edge points and selects a set of edge points that most closely resemble lanemarks.

This paper covers the application of hybrid vibro-acoustic simulation methods to shorten the design cycle of power-plant components. A comparison is made between Frequency Response Function based and Modal based algorithms for the generation of a predictive powerplant assembly model. The effectiveness of design modifications is evaluated by loading the original and modified predictive models with experimentally identified excitation forces. The procedure is validated by correlation with experimental data.

A hardening depth evaluation technique using eddy current has been developed, which can be applied to a mass production line for destructive (cutting) inspections. Using this technique, changes in the hardness of the induction-hardened structure can be detected based on the changes in magnetic permeability. This technique reduces the thermal effect and improves measurement accuracy through a multi-frequency exciting method and a difference method algorithm.

Toyota Motor Corporation has already designed and developed vehicle brake control systems for relatively low speed off-road driving, such as Downhill Assist Control, Hill-start Assist Control and Active Traction Control. Though off-road utility is improved by virtue of these systems, in specific situations actual performance still depends on driving technique since the driver is required to control the accelerator pedal. Toyota has integrated these existing systems, and developed a new driving technology for off-road driving called “Crawl Control.” Crawl Control automatically modulates brake torque and drive torque to help keep the vehicle speed constant and slow. Unskilled drivers can thereby attain improved capabilities in places where high-level driving techniques are required. This system also reduces the effort required to control the accelerator and the brake pedal. This paper presents a new control algorithm for the realization of this Crawl Control system.

In recent years, GPFs (Gasoline particulate filters) have been installed in gasoline engines to comply with stricter environmental regulations in China and Europe. In particular, coated-GPFs having a catalytic purification function are required to have high conversion performances, high filter efficiencies in the sense of a high collection efficiency, and low pressure loss. It is not easy to design a filter that satisfies all these parameters. Experimental studies are being conducted, but it is costly to study in trial productions. In this technical paper, a GPF design optimization method will be proposed that combines multi-scale simulation, surrogate models by machine learning, and an optimization algorithm. By using this method, a GPF design that minimizes pressure loss while providing high conversion performance and particle collection rates that satisfy current regulations can be created.

Conventional CFD-based shape optimization technology that uses parametric shape modification and optimal solutions searching algorithms has the two problems: (1) outcome of optimized shapes depend on the selection of design parameters made by the designer, and (2) high computational costs. To resolve those problems, two innovative inverse analysis technologies based on the Adjoint Method were developed in previous study: surface geometry deformation sensitivity analysis to identify the locations to be modified, and topology optimization to generate an optimal shape for maximizing the cost function in the constrained design space. However, these technologies are only applicable to steady flows. Since most flows in a vehicle (such as engine in-cylinder flow) are transient, a practical technology for surface geometry sensitivity analysis has been developed based on the Transient Adjoint Method.

This paper considers an application of reference governor (RG) to automotive diesel aftertreatment temperature control. Recently, regulations on vehicle emissions have become more stringent, and engine hardware and software are expected to be more complicated. It is getting more difficult to guarantee constraints in control systems as well as good control performance. Among model-based control methods that can directly treat constraints, this paper focuses on the RG, which has recently attracted a lot of attention as one method of model prediction-based control. In the RG, references in tracking control are modified based on future prediction so that the predicted outputs in a closed-loop system satisfy the constraints. This paper proposes an online RG algorithm, taking account of the real-time implementation on engine embedded controllers.

In real-world automotive control, there are many constraints to be considered. In order to explicitly treat the constraints, we introduce a model-prediction-based algorithm called a reference governor (RG). The RG generates modified references so that predicted future variables in a closed-loop system satisfy their constraints. One merit of introducing the RG is that effort required in control development and calibration would be reduced. In the preceding research work by Nakada et al., only a single reference case was considered. However, it is difficult to extend the previous work to more complicated systems with multiple references such as the air path control of a diesel engine due to interference between the boosting and exhaust gas recirculation (EGR) systems. Moreover, in the air path control, multiple constraints need to be considered to ensure hardware limits. Hence, it is quite beneficial to cultivate RG methodologies to deal with multiple references and constraints.

Statistical Energy Analysis (SEA) is a promising tool for developing an efficient sound package design for reducing airborne interior noise at high frequencies. The optimal sound package, however, is not directly predicted by using the SEA vehicle model alone and therefore requires parametric studies of sound package configurations. This paper describes an effective method for using SEA modeling to achieve the desired interior noise level targets. A mathematical model, expressed by one equation, is derived on the assumption that the directions of the power flows are known in the SEA model. This equation describes the relationship between sound package properties and the resulting interior noise level. Using the relationship between weight and performance of sound package, an efficient configuration can be determined. The predicted sound pressure level of the vehicle interior with the optimized sound package correlated well to the experimental data for the case presented in this paper.

Merging a CAE shape optimization system and a concept Taguchi method SN-ratio index, a robustness-focused automated shape optimization method has been developed. Applying this method to diesel intake ports, with mold position tolerance set as the error factor, SN-ratio was defined for swirl stability. As a result of the optimization provided by a multi-objective genetic algorithm, simultaneous improvement of flux, swirl rotation and SN ratio was achieved.