The growing demand for advanced vehicle features and zero-emission vehicles puts automotive engineering teams under increasing pressure to incorporate new technologies at an ever-faster pace. To meet these demands, more and more teams are moving to AI.

The rise of AI over the past decade has produced technologies that can be used at each stage of the engineering workflow. For example, machine learning models can be used to mine historical fleet data to inform key decisions at the design stage; they can also be deployed on the vehicle as part of an advanced driver assistance system (ADAS).

Many recent innovations would not be possible without AI techniques. But not all AI techniques are new to automotive engineers. For example, advanced statistical models (machine learning models in today’s parlance) have long been used to characterize test cell data in calibration workflows.

This paper shows how automotive teams use AI to:

• Build innovative features
• Enhance existing products
• Improve development workflows
• Interpret real-world data
• Perform process improvement and service enhancement

Case studies provide real-world examples of how engineering organizations are using AI in automated driving, powertrain and controls, fault diagnosis and prediction, and manufacturing.

Building Innovative Features

Mahindra Truck and Bus: Using Machine Learning to Estimate Road Surface Conditions

Mahindra Truck and Bus Division developed a tool that uses data from sensors on the vehicle, applies signal filtering and sensor fusion to generate features from the data, and then applies a machine learning model to predict the road surface condition. This prediction is incorporated into other algorithms and is used to improve the fuel economy of the vehicle.

Automated Driving

Perhaps the field where AI techniques have had the largest impact is in automated driving. The ability of machine learning and deep learning models to perceive the surrounding environment has made them an essential tool in the push to higher levels of autonomy. These models are used to translate data from cameras, lidar, radar, and other vehicle sensors into meaningful representations of the environment. Building such models requires a few foundational pieces: labeled ground-truth data that can be used to train models, algorithms for fitting models to data, and a framework for validating the performance of the models. Because real-world data plays an important role in building such models, much of the work involved relates to obtaining and curating the data sets.

Caterpillar: Managing Ground-Truth Data

Caterpillar, in collaboration with bat365, developed a big data infrastructure with a web front end to leverage external labelers; a database for searching and retrieving labeled ground truth; and an interface that enables function developers to use the labeled ground truth for training, validating, and deploying classifiers.

Continental: Traffic Sign Recognition from Camera Data

Applying AI techniques to camera data requires a complete workflow from data curation through model development and deployment. Tools are also needed to visualize the performance of the model to understand where its strengths and weaknesses are. Engineers at Continental developed a tool chain to label ground-truth data, inspect recorded scenes, and develop and validate machine learning algorithms for traffic sign recognition.

PathPartner: Classifying Objects in Radar Point Clouds

Engineers at PathPartner applied machine learning to radar data in an application that can detect pedestrians and other vulnerable road users in conditions where cameras may be insufficient, such as at night, in inclement weather, and at a distance. They used the Classification Learner app to quickly assess multiple machine learning algorithms and determine prediction accuracy.

Autoliv: Object Detection from Lidar Point Clouds

Engineers at Autoliv used deep learning to detect objects in lidar point clouds, enabling them to identify objects in zones that are undetectable by other sensors. With deep learning, the team significantly reduced the time to manually label and analyze lidar data.

Enhancing the Performance of Existing Products

BMW: Detecting Oversteer

BMW developed a machine learning model for detecting oversteer, a situation in which a vehicle’s rear tires lose their grip while navigating a turn. Using data collected from a driver on a track, the team analyzed the data from various sensors on the vehicle to identify the ones that would be most useful for detecting oversteer. They then trained a machine learning model that could perform this detection on new data and deployed it to the ECU for on-vehicle testing. This on-vehicle testing not only proved the machine learning workflow was viable, but also enabled the team to assess the performance of the overall traction control system and understand the improvements compared with the previous design.

Cummins: Developing System Identification Models for Control Design

Cummins (15:53) is researching the use of machine learning to enhance classical control design. Their investigation includes the use of machine learning to improve system identification models for model predictive control and combining data-driven techniques such as deep learning and reinforcement learning with PID-based control.

Vitesco: Reinforcement Learning for Emissions Reduction

Vitesco applied reinforcement learning in an emissions application. After creating a detailed model of the plant (consisting of the engine and exhaust gas system), they prototyped, generated, and optimized reinforcement learning agents to improve the control strategy for an exhaust gas aftertreatment system.

Improving Development Workflows

Mazda: Using Model-Based Calibration to Reduce Testing and Increase Model Accuracy

Model-based calibration consists of using traditional statistics methods to define a test plan, capturing data from an experimental or simulated setup, fitting a data-based model to the data, and then optimizing system performance using the data-based model as a proxy for the actual system. Using model-based calibration techniques, Mazda engineers minimized the testing workload, reduced the engine model complexity, and increased the accuracy of their model for predicting smoke from the exhaust pipe.

GM: Using Model-Based Calibration for Electric Drive Systems

The same model-based calibration techniques are also being used to calibrate electric drive systems. General Motors calibrated their current reference generation tables to determine the optimal current command for each speed/torque/voltage combination in an electric drive system. They used design of experiments to come up with an optimal testing plan, then fit a variety of machine learning models to the experimental data to model the current as a function of the inputs. These current models supported rapid design exploration, enabling the team to perform an optimization that generated the current reference tables to be used in the vehicle software.

AI-Assisted Testing

AI techniques can be used to reduce the amount of human oversight needed during testing. Using historical test data, models can be trained to identify desirable or undesirable behavior while a test is running. Because AI models can be trained on sensor data such as images, audio, or video, they can be used to perform real-time classification of testing criteria.

Daihatsu: Identifying Engine Knock

Identifying knocking is a task that has traditionally been performed by skilled testers. Daihatsu engineers developed an approach that used acoustic analysis to extract features from audio signals. These features are then fed into a deep learning model that classifies engine knock. This approach can judge the knocking sound with the same accuracy as skilled workers.

Reducing Emissions

Legacy automakers are under increasing pressure to make zero-emissions vehicles (powered by batteries or fuel cells) while continuing to develop conventional vehicles equipped with internal combustion engines (ICEs). Stringent clean air regulations are driving the development of more complex ICE- and catalytic converter–based systems and on-board diagnostics (OBD) systems. These systems tend to be nonlinear, and developing, tuning, and testing them requires considerable resources. AI approaches to developing data-driven models offer a viable alternative by enabling the increased use of virtual environments for development.

Renault: Developing an Exhaust Gas Estimator

Traditional NO_X estimates come from lookup tables or combustion models. However, lookup tables lack accuracy and combustion models are computationally expensive. Engineers at Renault used a deep learning network to model engine-out NO_X. They collected experimental data from an actual engine with common drive cycles, and iterated over many deep neural network configurations to find one that suitably modeled the engine-out NO_X. They then used this model as part of a nonlinear observer for control design and for desktop simulation.

Interpreting Real-World Data

Honda: Evaluating Emissions Aftertreatment Systems

Engineers at Honda (19:33) analyzed 1 million kilometers of driving data from 1,000 vehicles to inform development activities for meeting emission regulations. They developed a pipeline to preprocess and filter the raw data then extract features including speed, mileage, gear selection, AC utilization, and traffic conditions. The statistical distributions derived from this analysis enabled the engineers to ensure that their emissions aftertreatment systems met performance requirements.

Volkswagen Data Lab: Analyzing Driving Patterns

Volkswagen Data Lab analyzed how telemetry data varied from driver to driver and developed a classifier that could automatically determine who was driving based on data from the vehicle sensors. They used CAN bus data from the vehicle and extracted various statistical features from the time series using sliding windows. This approach enabled them to do statistical recognition of the driver, providing a basis for potential business models such as “pay-as-you-drive.”

Real-World Fleet Data

Connected vehicles generate real-world driving data that can be used in many aspects of vehicle design. The amount of data can be so large that specialized IT systems are required to store it.

Ford: Accessing and Analyzing Fleet Data

A team at Ford connected MATLAB to their fleet data stored on Apache Spark™ so that engineers can access and analyze this data with familiar tools. This data enables ADAS engineers to understand how a feature is performing in the real world and to generate scenarios that can be used for simulation and validation.

Process Improvement and Service Enhancement

Mercedes-Benz: Detecting Machine Cycle Errors

Engineers at Mercedes-Benz applied anomaly detection techniques to detect error cycles in manufacturing production parameters. They used time-series techniques to identify characteristics of machine cycles and then performed statistical analysis to characterize normal and anomalous cycles.

Predictive Maintenance

Predictive maintenance techniques are also being used for on-vehicle applications. Especially in the commercial vehicle space, unplanned downtime can be costly for equipment operators. The traditional way to address this problem is to schedule routine maintenance far in advance of a failure occurring. However, this approach requires extra labor for the maintenance and may result in the scrapping of parts that have remaining useful life.

Baker Hughes: Predicting Pump Failure

Baker Hughes used machine learning techniques to develop a predictive maintenance algorithm that can predict the failure of pumps on oil extraction equipment, enabling them to reduce unplanned failures and save an estimated $10 million a year.