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The Advanced Driver Assistance System (ADAS) is a comprehensive feature set designed to aid a driver in avoiding or reducing the severity of collisions while operating the vehicle within specified conditions. In General Motors (GM) vehicles, the primary controller for the ADAS is the Active Safety Control Module (ASCM). In the 2013 model year, GM introduced an ASCM utilizing the GM internal nomenclature of External Object Calculation Module (EOCM) in some of their vehicles produced for the North American market. Similar to the Sensing and Diagnostic Module (SDM) utilized in the restraints system, the EOCM3 LC contains an Event Data Recorder (EDR) function to capture and record information surrounding certain ADAS or Supplemental Inflatable Restraint (SIR) events. The ASCM EDR contains information from external object sensors, various chassis and powertrain control modules, and internally calculated data.

Recognizing road conditions using onboard sensors is significant for the performance of intelligent vehicles, and the road profile is a widely accepted representation both in the temporal and frequency domains, greatly influencing driving quality. In this paper, a recurrent neural network embedded with attention mechanisms is proposed to reconstruct the road profile sequence. Firstly, the road and vehicle sensor signals are obtained in a simulated environment by modeling the road, tire, and vehicle dynamic system. After that, the models under different working conditions are trained and tested using the collected data, and the attention weights of the trained model are then visualized to optimize the input channels. Finally, field experiments on the real vehicle are conducted to collect real road profile data, combined with vehicle system simulation, to verify the performance of the proposed method.

The proposed Euro 7 regulation includes On Board Monitoring, or OBM, to continuously monitor vehicles for emission exceedances. OBM relies on feedback from existing or additional sensors to identify high emitting vehicles, which poses many challenges. Currently, sensors are not commercially available for all emissions constituents, and the accuracy of available sensors is not capable enough for in use compliance determination. On board emissions models do not offer enough fidelity to determine in use compliance and require new complex model innovation development which will be extremely complicated to implement on board the vehicle. The stack up of multi-component deterioration leading to an emissions exceedance is infeasible to detect using available sensors and models.

The subsystem of front of dash (FOD) and instrument panel (IP) is a critical path to isolate the powertrain noise and road noise for vehicles. This subsystem mainly consists of sheet metal, dash mats, IP, and the components inside IP such as HVAC and wiring harness. To achieve certain level of cabin quietness, the sound transmission loss performance of this subsystem is usually used as a quantifier. In this paper, the sound transmission loss through the FOD and IP is investigated up to 10kHz, through both acoustic testing and numerical simulation. In the acoustic testing, the subsystem is cut from a vehicle and installed on the wall of two-rooms STL testing suite, with source room being reverberant and receiver room being anechoic. In the testing, various scenarios are measured to understand the contributions from different components.

General Motors (GM) is working towards a future world of zero crashes, zero emissions and zero congestion. It’s “Ultium” platform has revolutionized electric vehicle drive units to provide versatile yet thrilling driving experience to the customers. Three variants of traction power inverter modules (TPIMs) including a dual channel inverter configuration are designed in collaboration with LG Magna e-Powertrain (LGM). These TPIMs are integrated with other power electronics components inside Integrated power electronics (IPE) to eliminate redundant high voltage connections and increase power density. The developed power module from LGM has used state-of-the art sintering technology and double-sided cooled structure to achieve industry leading performance and reliability. All the components are engineered with high level of integration skills to utilize across TPIM variants.

This paper proposes an intelligent car testing and evaluation method based on digital twins, which is crucial for ensuring the proper functioning of autonomous driving systems. This method utilizes digital twin testing technology to effectively map and integrate real vehicles in real-world testing scenarios with virtual test environments. By enriching the testing and validation environment for smart cars, this approach improves testing efficiency and reduces costs. This study connects real test vehicles with simulation software testing toolchains to build a digital twin autonomous driving testing platform. This platform facilitates the validation, testing, and evaluation of functional algorithms, and case study is conducted through testing and validation of an emergency collision avoidance system. By rapidly applying digital twin testing and evaluation techniques for intelligent cars, this approach accelerates the development and deployment of autonomous vehicles.

De-centralized brake actuation – that is, brake systems that incorporate individual actuators at each wheel brake location to both provide the apply energy and the modulation of braking force – is not a new area of study. Typically realized in the form of electro-mechanical brake calipers or drum brakes, or as “single corner” hydraulic actuators, de-centralized actuation in braking systems has already been deployed in production on General Motor EV1 Electric Vehicle (1997) in the form of electric drum brakes and has been studied continually by the automotive industry since then. It is frequently confused with “brake by wire,” and indeed practical implementations of de-centralized actuation are a form of brake by wire technology. However, with millions of vehicles on the road already with “brake by wire” systems - the vast majority of which have centralized brake actuation – the future of “brake by wire” is arguable settled.

The development of intelligent and networked vehicles has enhanced the demand for precise road information perception. Detailed and accurate road surface information is essential to intelligent driving decisions and annotation of road surface semantics in high-precision maps. As one of the key parameters of road information, road roughness significantly impacts vehicle driving safety and comfort for passengers. To reach a rapid and accurate estimation of road roughness, in this study, we develop a neural network model based on vehicle response data by optimizing a long-short term memory (LSTM) network through the particle swarm algorithm (PSO), which fits non-linear systems and predicts the output of time series data such as road roughness precisely. We establish a feature dataset based on the vehicle response time domain data that can be easily obtained, such as the vehicle wheel center acceleration and pitch rate.

The interaction between driveline control and anti-lock braking system (ABS) control in electric vehicles (EV) was investigated based on multi-body dynamics (MBD) model and control model co-simulation. Two primary driveline control algorithms, active damping control and wheel flare control, were integrated with ABS control in Simulink model and the influence on ABS control was studied. The event for high mu to low mu transition was simulated. When ABS control is active on low mu surface, the vehicle shows large wheel slip and long duration time before wheel speed returns to stable control. This performance could be improved with activating driveline control. Deceleration uniformity metric shows that active damping control has very small effect when ABS control becomes stable after passing through the high mu to low mu transition period. Driveline damping control can help to reduce vibration, but it is difficult to find satisfied tuning for wheel speed performance.

The front camera module is a fundamental component of a modern vehicle’s active safety architecture. The module supports many active safety features. Perception of the road environment, requests for driver notification or alert, and requests for vehicle actuation are among the camera software’s key functions. This paper presents a novel method of testing these functions virtually. First, the front camera module software is compiled and packaged in a Docker container capable of running on a standard Linux computer as a software in the loop (SiL). This container is then integrated with the active safety simulation tool that represents the vehicle plant model and allows modeling of test scenarios. Then the following simulation components form a closed loop: First, the active safety simulation tool generates a video data stream (VDS). Using an internet protocol, the tool sends the VDS to the camera SiL and other vehicle channels.

Vehicle manufacturers conduct tests to develop crash sensing system calibrations. Ditch fall-over is one of a suite of laboratory tests used to develop rollover sensing calibrations that can trigger deployment of safety devices like roof rail airbags and seat belt pretensioners. The ditch fall-over test simulates a flat road followed by a ditch on one side of the road. The vehicle heads into the ditch and the driver applies swift steer input once the ditch slope is sensed. Typically, the steer input is applied when the two down-slope wheels on the ditch side enter the ditch. Multi-Body Dynamics (MBD) software can be used for virtual simulation of these test events. Conventionally in simulations, the vehicle-model is run without steer input and the marking line crossing time is observed/manually recorded from observation of simulation video. This recorded time is used to apply the steer input and the full event is then re-simulated.

The Continuous Fluid Level and Quality Indicator (CFLQI) technology is focused on increasing the sampling frequency of brake fluid reservoir volume and detecting specific brake fluid contaminants. CFLQI targets to improve diagnostics detection range and resulting degraded vehicle operation strategies by increasing sensitivity to brake fluid loss and the addition of a fluid quality feature. The theory of CFLQI is to improve future autonomous and highly automated vehicle performance, both of which will have reduced driver input and service schedules, by providing earlier fluid level and fluid health warnings. The two technologies selected to prove theory of operation were ultra-sonic sensor and capacitive sense element technology. Both technologies show initial capability to meet fluid sensing targets with system level ASIL D ASIC design. The CFLQI compliments and improves upon current technology of brake pad wear sensors, leak detection diagnostics and brake fluid level monitoring.

The validation of brake discs has remained, to this day, heavily reliant on “Thermal Abuse” or “Thermal Cracking” type testing, with many procedures so dated that most engineers active in the industry today cannot even recall the origin of the test. These procedures - of which there are many variants - all share the trait of greatly accelerating durability testing by performing repeated high power (high speed and high deceleration) brake applies to drive huge temperature gradients and internal stress, and often allowing the disc to get very hot, to where the strength of the material from which the disc is constructed is significantly degraded. There is little debate about whether these procedures work; by and large disc durability issues in the field are extremely rare.

Regenerative braking without question greatly impacts brake pad service life in the field, in most cases extending it significantly. Estimating its impact precisely has not been an overriding concern - yet - due in part to the extensive sharing of brake components between regen-intensive battery-electric and hybrid vehicles, and their more friction-brake intensive internal combustion engine powered sibling. However, a multitude of factors are elevating the need for a more accurate estimation, including the emerging of dedicated electric vehicle architectures with opportunities for optimizing the friction brake design, a sharp focus on brake particulate emissions and the role of regenerative braking, a need to make design decisions for features such as corrosion protection for brake pad and pad slide components, and the emergence of driver-facing features such as Brake Pad Life Monitoring.

Analyzing low frequency brake noise (< 300Hz) has been challenging due to the difficulty associated with calculating dynamic friction behavior and its multiple structure-borne noise transfer paths. In theory, it is possible to simulate sound pressure level inside the cabin by calculating a transfer function between friction excitation, which is on the interface between rotor and pads, and cabin acoustic response, and by multiplying dynamic friction force at the rotor-pad interface to that transfer function. However, calculating the dynamic friction forces when brake noise occurs has been one of the most challenging research topics in the brake community. This paper describes a novel concept to simulate sound pressure level inside the cabin without knowing the dynamic friction forces at the rotor-pad interface in advance.

As the automotive industry prepares to roll out an unprecedented range of fully electric propulsion vehicle models over the next few years - it really brings to a head for folks responsible for brakes what used to be the subject of hypothetical musings and are now pivotal questions for system design. How do we really go about designing brakes for electric vehicles, in particular, for the well-known limit condition of descending a steep grade? What is really an “optimal’ design for brakes considering the imperatives for the entire vehicle? What are the real “limit conditions” for usage that drive the fundamental design? Are there really electric charging stations planned for or even already existing in high elevations that can affect regenerative brake capacity on the way down? What should be communicated to drivers (if anything) about driving habits for electric vehicles in routes with significant elevation change?

The modern braking system in the field today may be controlled by over a million lines of computer code and may feature several hundred moving parts. Although modern brake systems generally deliver performance, even with partial failures present in the system, that is well above regulatory minimums, they also have a level of complexity that extends well beyond what the authors of existing regulations had envisioned. Complexity in the braking system is poised for significant increases as advanced technologies such as self-driving vehicles are introduced, and as multiple systems are linked together to provide vehicle-level “features” to the driver such as deceleration (which can invoke service braking, regenerative braking, use of the parking brake, and engine braking). Rigorous safety-case analysis is critical to bring a new brake system concept to market but may be too tedious and rely on too many assumptions to be useful in the early architecting stages of new vehicle development.

Accurate measurements of brake friction materials are critical to understanding brake behaviors during testing. Current methods typically utilize a hand gauge (or a machine, in some cases) to sample various discrete points on the brake lining. This approach limits measurements to planar wear characteristics, taper and thickness, and excludes more complex measurements such as cupping. The limited number of points means that a single errant point measurement or the choice of point locations can have a large impact on the reported wear measurement. This paper will describe a method for utilizing a Coordinate Measurement Machine (CMM) fitted with a laser line scanning tool to generate a point cloud of data that can then be compared to an earlier measurement of the same piece or to a math model. This method produces thousands of data points which allows for more accurate volumetric wear calculations and color maps of the entire friction face.

The automotive industry is continuously striving to reduce vehicle mass by reducing the mass of components including wheel bearings. A typical wheel bearing assembly is mostly steel, including both the wheel and knuckle mounting flanges. Mass optimization of the wheel hub has traditionally been accomplished by reducing the cross-sectional thickness of these components. Recently bearing suppliers have also investigated the use of alternative materials. While bearing component performance is verified through analysis and testing by the supplier, additional effects from system integration and performance over time also need to be comprehended. In a recent new vehicle architecture, the wheel bearing hub flange was reduced to optimize it for low mass. In addition, holes were added for further mass reduction. The design met all the supplier and OEM component level specifications.

Passenger vehicles have made astounding technological leaps in recent years. Unfortunately, little of that progress has trickled down to other segments of the transportation industry leaving opportunities for massive gains in safety and performance. In particular, the electric drum brakes on most consumer trailers differ little from those on trailers over 70 years ago. Careful examination of current production passenger vehicle hardware and trailering provided the opportunity to produce a design and test vehicle for a plausible, practical, and performant trailer braking system for the future. This study equips the trailer with high control frequency antilock braking and dynamic torque distribution through use of passenger vehicle grade apply hardware.