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The SAE AutoDrive Challenge II is a four-year collegiate competition dedicated to developing a Level 4 autonomous vehicle by 2025. In January 2023, the participating teams each received a Chevy Bolt EUV. Within a span of five months, the second phase of the competition took place in Ann Arbor, MI. The authors of this contribution, who participated in this event as team Wisconsin Autonomous representing the University of Wisconsin–Madison, secured second place in static events and third place in dynamic events. This has been accomplished by reducing reliance on the actual vehicle platform and instead leveraging physical analogs and simulation. This paper outlines the software and hardware infrastructure of the competing vehicle, touching on issues pertaining sensors, hardware, and the software architecture employed on the autonomous vehicle. We discuss the LiDAR-camera fusion approach for object detection and the three-tier route planning and following systems.

Powertrain development requires an efficient development process with no rework and model-based development (MBD). In addition, to performance design that achieves low CO2 emissions is also required. Furthermore, it also demands fuel economy performance considering real-world usage conditions, and in North America, the EPA (U.S. Environmental Protection Agency) 5-cycle, which evaluates performance in a combination of various environments, is applied. This evaluation mode necessitates predicting performance while considering engine heat flow. Particularly, simulation technology that considers behavior based on engine temperature for Hybrid Electric Vehicle (HEV) is necessary. Additionally, in the development trend of vehicle aerodynamic improvement, variable devices like Active Grille Shutter (AGS) are utilized to contribute to reducing CO2 emissions.

Identifying edge cases for designed algorithms is critical for functional safety in autonomous driving deployment. In order to find the feasible boundary of designed algorithms, simulations are heavily used. However, simulations for autonomous driving validation are expensive due to the requirement of visual rendering, physical simulation, and AI agents. In this case, common sampling techniques, such as Monte Carlo Sampling, become computationally expensive due to their sample inefficiency. To improve sample efficiency and minimize the number of simulations, we propose a tailored active learning approach combining the Support Vector Machine (SVM) and the Gaussian Process Regressor (GPR). The SVM learns the feasible boundary iteratively with a new sampling point via active learning. Active Learning is achieved by using the information of the decision boundary of the current SVM and the uncertainty metric calculated by the GPR.

There is significant potential for connected and autonomous vehicles to impact vehicle efficiency, fuel economy, and emissions, especially for hybrid-electric vehicles. These improvements could have large-scale impact on oil consumption and air-quality if deployed in large Mobility-as-a-Service or ride-sharing fleets. As part of the US Department of Energy's current Advanced Vehicle Technology Competition (AVCT), EcoCAR: The Mobility Challenge, Mississippi State University’s EcoCAR Team is redesigning and doing the development work necessary to convert a conventional gasoline spark-ignited 2019 Chevy Blazer into a hybrid-electric vehicle with SAE Level 2 autonomy. The target consumer segments for this effort are the Mobility-as-a-Service fleet owners, operators and riders. To accomplish this conversion, the MSU team is implementing a P4 mild hybridization strategy that is expected to result in a 30% increase in fuel economy over the stock Blazer.

Since the operation of the powertrain system and the engine speed and torque are determined in the ECU in hybrid vehicles, control parameters in these vehicles are more sensitive to a variety of performance factors than those employed in conventional vehicles. The three performance factors acceleration performance, NVH and fuel consumption in particular are in a tradeoff relationship, the calibration of control parameters in order to satisfy these performance targets entail considerable development costs. Given this, it is possible to increase the efficiency of hybrid vehicle development by determining Pareto design solutions for the three performance factors via multi-objective optimization using CAE, and selecting target performance and control parameters based on these Pareto design solutions.

4C3B (4 coat 3 bake) painting system (see Figure 1) which needs a bake process after the primer surfacer paint was very general and common process for the automotive body painting system. In the beginning of the 2000s, 4C2B painting system (Reference 1) was developed which changed the oven after the primer surfacer paint to a pre heat area, so it can reduce the carbon dioxide (Figure 1, and Figure 2). But unfortunately in this 4C2B painting system, the base coat will be painted on the primer surfacer paint wet-on-wet. By that reason, the appearance deterioration will occur often. The authors used a low temperature crosslinking agent “Polycarbodiimide” to a water born primer surfacer paint, to control the viscosity of primer surfacer paint at the pre heat area. Controlling the viscosity is important to avoid the layer mixing of the primer surfacer paint and the base coat which makes appearance deterioration.

In motorcycles, the mass difference between a vehicle and a rider is small and motions of a rider impose a great influence on the vehicle behaviors as a consequence. Therefore, dynamic properties of motorcycles should be evaluated not merely dealing with a vehicle but considering with a man-machine system. In the studies of a simulation for vehicle dynamics, various types of rider models have been proposed and it has already been reported that rider motions have a significant influence on the dynamic properties. However, the mechanism of the interaction between a rider and a vehicle has not been clarified yet. In our study, we focused on weave motion and constructed a full vehicle simulation model that can reflect the influences of the movements of the rider’s upper body and lower body. To construct the rider model, we first measured the vibrational characteristics of a human body using a vibration test bench.

Lane departure prevention systems are able to detect imminent departure from the road, allowing the driver to apply control to prevent lane departure. These systems possess enormous potential to reduce the number of accidents resulting from road departure, but their effectiveness is highly reliant on their level of acceptance by drivers. The effectiveness of the systems will depend on when they are providing driving assistance, what level of laxness in terms of maintaining contact with the steering wheel is allowed on the part of the driver, and what level of assistance the system provides. This paper will discuss research on the minimum necessary contact and contact strength with the steering wheel on the part of the driver when a lane departure prevention system is in operation.

Along with the suspension improvement in these two decades, it is well known that the suspension friction force became one of major parameters to affect ride comfort performance. However, it was difficult to carry out quantitative prediction on ride comfort improvement against friction force change with high correlation. It was difficult to analyze correlation between actual vehicle performance and simulation since there were difficulties in controlling damping force and friction individually. On the other hand, magneto-rheological shock absorber (MR Shock) has had several applications and widely spread to passenger vehicles. The large variation and high response of damping force especially in slow piston speed region contributes to achieve an excellent vehicle dynamics performance. However, MR Shock shows the high friction characteristics, due to the unique sliding regime of internal parts. It is said that this high friction characteristic is causing obstacles in ride-comfort.

Vehicles are required durability in various environments all over the world. Especially water resistance on flooded roads is one of the important issues. To solve this kind of problem, a CFD technology was established in order to predict the water resistance performance of the vehicle at the early development stage. By comparison with vehicle tests on flooded roads, it is clarified the following key factors are required for accurate prediction; the vehicle velocity change, the vehicle height change and the air intake flow rate. Moreover, these three key factors should be appropriately determined from vehicle and engine specification to predict water intrusion for flooded roads at the early stage of development. In this paper, a methodology which determines appropriate analysis conditions mentioned above for flooding simulation from vehicle and engine specification is described. The methodology enables us to determine whether the vehicle provides sufficient waterproofness.

Conventionally, it has not been possible to evaluate current and temperature in power control units (PCU) for hybrid electric vehicles (HEV) during vehicle operation without using an actual permanent magnet synchronous motor (PMSM). The research discussed in this paper developed a motor emulator to take the place of an actual motor, making it possible to conduct tests for the evaluation of current and temperature in PCU during vehicle operation without the need to use a motor. The motor emulator is provided with a hardware-in-the-loop (HIL) simulator that calculates motor models at high speed using a field programmable gate array (FPGA). The developed system models the motor in detail via the HIL simulator, while a 3-phase current generator accurately reproduces the transient current in the PCU during vehicle operation.

A method applicable in the design stage to predict fatigue strength of a motorcycle exhaust system was developed. In this prediction method, a vibrating stress, thermal stresses, stresses resulting from the assembling of the exhaust system components and a deterioration of fatigue strength of materials originated from high temperature were simultaneously taken into account. For the prediction of the vibrating stress, flexible multibody dynamics was applied to get modeling accuracy for vibration characteristics of the entire motorcycle and the exciting force delivered from engine vibrations. The thermal conduction analysis and the thermal deformation analysis based on finite element method (FEM) were applied for the prediction of thermal stresses in the exhaust system components. The temperature distribution on the surfaces of the exhaust system components is required for calculations of the thermal stresses.

According to the North American National Automotive Sampling System Crashworthiness Data System (NASS/CDS), approximately one-half of all accidents during driving are of the secondary collision pattern in which the collision event involves the occurrence of secondary collision. Accidents involving impact to a stopped vehicle (chain-reaction collisions) have increased to approximately 3% of all accidents in North America, and although the rate of serious injury is low, cases have been reported of accidents in which cervical sprain occurs as an after-effect[1]. In order to mitigate these circumstances, research has been conducted on systems of automatic braking for collisions. These systems apply brakes automatically when a first collision has been detected in order to avoid or lessen a second collision. Research on automatic collision braking systems, however, has not examined the multiple collisions parked [1, 2].

The aim discussed in this paper is to show a technique to predict loads input to the wheels, essential to determining input conditions for evaluation of suspension durability, by means of full vehicle simulations using multi body analysis software Adams/Car. In this process, model environments were built to enable reproduction of driving modes, and a method of reproducing the set-up conditions of a durability test vehicle was developed. As the result of verification of the accuracy of the simulations in the target driving modes, good correlation for waveforms can be confirmed. And also confirm a good correlation in relation to changes of input load due to changes in suspension specifications.

The change in the aerodynamic lift force (henceforth CL) by heave motion is discussed in this paper in order to clarify the effect of aerodynamic characteristics on the vehicle dynamic performance. We considered that phenomenon in actual car running at 160km/h and 1Hz heave frequency. Using a towing tank to change its water from the air to the working fluid to more easily observe this phenomenon. That makes possible to observe the same phenomenon with reduced velocity and small models under same Strouhal number condition. This method can be reducing vehicle speed to 3m/s (1/15 actual) and frequency to 0.2Hz (1/5 actual) in case using 40% scaled model. The results of these tests showed that unsteady CL is proportional to heave motion. These results showed the proportional relationship between unsteady CL and heave motion. The formularization of unsteady CL made it possible to introduce shape coefficients to vehicle dynamics simulations as functions of heave velocity.

Predicting fuel consumption and performance of an outboard motor for a high speed small planing boat are numerically challenging. The propeller is one of the most popular propulsion systems used for outboard motors. We focused our attention on the fact that the thrust performance of a propeller has a major impact on cruising fuel consumption and performance. We believe that we can numerically predict cruising fuel consumption, which has conventionally been estimated through experiential means, using accurate thrust performance measurements via CFD simulation without cavitations model. This study aims to develop a simulator that could quantitatively predict cruising fuel consumption and performance of an outboard motor used for a high speed small planing boat. After comparing the CFD simulation of propellers against the results of model tests, the simulated results are in good agreement with the experimental results.

The focus of this paper is to discuss the modeling and control of intake plenum pressure on the Powertrain Control Research Laboratory's (PCRL) Single-Cylinder Engine (SCE) transient test system using a patented device known as the Intake Air Simulator (IAS), which dynamically controls the intake plenum pressure, and, subsequently, the instantaneous airflow into the cylinder. The IAS exists as just one of many devices that the PCRL uses to control the dynamic boundary conditions of its SCE transient test system to make it “think” and operate as though it were part of a Multi-Cylinder Engine (MCE) test system. The model described in this paper will be used to design a second generation of this device that utilizes both continuously and discretely actuating valves working in parallel.

In recent years, the increased use of electric power steering in vehicles has increased the importance of issues such as making systems more compact and lightweight, and dealing with increased development man-hours. To increase development efficiency, the use of a “Hardware in the loop simulator” (HILS) is being tested to shift from the previous development method that relied on a driver's subjective evaluation in an actual vehicle test to bench-test development. Using HILS enables tasks such as specification studies, performance forecasts, issue identification and countermeasure proposals to be performed at an early stage of development even when there is no prototype vehicle. This report describes a case study of using HILS to solve the issues of reducing the load by adjusting the geometric specifications around the kingpin and eliminating the tradeoff by adding a new EPS control algorithm in order to make the electric power steering (EPS) more compact and lightweight.

The development of the WorldSID 50th percentile male dummy was initiated in 1997 by the International Organization for Standardization (ISO/SC12/TC22/WG5) with the objective of developing a more biofidelic side impact dummy and supporting the adoption of a harmonized dummy into regulations. More than 45 organizations from all around the world have contributed to this effort including governmental agencies, research institutes, car manufacturers and dummy manufacturers. The first production version of the WorldSID 50th male dummy was released in March 2004 and demonstrated an improved biofidelity over existing side impact dummies. Full-scale vehicle tests covering a wide range of side impact test procedures were performed worldwide with the WorldSID dummy. However, the vehicle safety performance could not be assessed due to lack of injury risk curves for this dummy. The development of these curves was initiated in 2004 within the framework of ISO/SC12/TC22/WG6 (Injury criteria).

By adapting vehicle control systems to the skill level of the driver, the overall vehicle active safety provided to the driver can be further enhanced for the existing active vehicle controls, such as ABS, Traction Control, Vehicle Stability Enhancement Systems. As a follow-up to the feasibility study in [1], this paper provides some recent results on data-driven driving skill characterization. In particular, the paper presents an enhancement of discriminant features, the comparison of three different learning algorithms for recognizer design, and the performance enhancement with decision fusion. The paper concludes with the discussions of the experimental results and some of the future work.