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Accurately predicting the future trajectories of surrounding traffic agents is important for ensuring the safety of autonomous vehicles. To address the scenario of frequent interactions among traffic agents in the highway merging area, this paper proposes a trajectory prediction method based on interactive graph attention mechanism. Our approach integrates an interactive graph model to capture the complex interactions among traffic agents as well as the interactions between these agents and the contextual map of the highway merging area. By leveraging this interactive graph model, we establish an agent-agent interactive graph and an agent-map interactive graph. Moreover, we employ Graph Attention Network (GAT) to extract spatial interactions among trajectories, enhancing our predictions. To capture temporal dependencies within trajectories, we employ a Transformer-based multi-head self-attention mechanism.

This paper proposes a vehicle performance/safety method using combined active steering and differential braking to achieve yaw stability and rollover avoidance. The advantages and disadvantages of active steering and differential braking control methods are identified under a variety of input signals, such as J-turn, sinusoidal, and fishhook inputs by using the implemented linear 4 DOF model. Also, the nonlinear model of the vehicle is evaluated and verified through individual and integrated controller. Each controller gives the correction steering angle and correction moment to the simplified steering and braking actuators. The integrated active steering and differential braking control are shown to be most efficient in achieving yaw stability and rollover avoidance, while active steering and differential braking control has been shown to improve the vehicle performance and safety only in yaw stability and rollover avoidance, respectively.

In electrified vehicle applications, the heat generated of lithium-ion (Li-ion) cells may significantly affect the vehicle range and state of health (SOH) of the pack. Therefore, a major design task is creation of a battery thermal management system with suitable control and cooling strategies. To this end, the thermal behavior of Li-ion cells at various temperatures and operating conditions should be quantified. In this paper, two different commercial pouch cells for plug-in hybrid electric vehicles (PHEVs) are studied through comprehensive thermal performance tests. This study employs a fractional factorial design of experiments to reduce the number of tests required to characterize the behavior of fresh cells while minimizing the effects of ageing. At each test point, the effects of ambient temperature and charge/discharge rate on several types of cell efficiencies and surface heat generation are evaluated.

Safety assurance is a central concern for the development and societal acceptance of automated driving (AD) systems. Perception is a key aspect of AD that relies heavily on Machine Learning (ML). Despite the known challenges with the safety assurance of ML-based components, proposals have recently emerged for unit-level safety cases addressing these components. Unfortunately, AD safety cases express safety requirements at the system level and these efforts are missing the critical linking argument needed to integrate safety requirements at the system level with component performance requirements at the unit level. In this paper, we propose the Integration Safety Case for Perception (ISCaP), a generic template for such a linking safety argument specifically tailored for perception components. The template takes a deductive and formal approach to define strong traceability between levels.

Traditional electrode materials for lithium-ion storage cells are typically crystalline layered structures such as metal oxides, and graphitic carbons. These materials power billions of portable electronic devices in today's society. However, large-scale, high-capacity storage devices capable of powering hybrid electric vehicles (HEV″s) or their plug-in versions (PHEV's) have much more demanding requirements with respect to safety, cost, and the power they must deliver. Recently, nanostructured solid state materials, which are comprised of two more compositional or structural phases, have been found to show exciting possibilities to meet these criteria.

Driving Automation Systems (DAS) are subject to complex road environments and vehicle behaviors and increasingly rely on sophisticated sensors and Artificial Intelligence (AI). These properties give rise to unique safety faults stemming from specification insufficiencies and technological performance limitations, where sensors and AI introduce errors that vary in magnitude and temporal patterns, posing potential safety risks. The Safety of the Intended Functionality (SOTIF) standard emerges as a promising framework for addressing these concerns, focusing on scenario-based analysis to identify hazardous behaviors and their causes. Although the current standard provides a basic cause-and-effect model and high-level process guidance, it lacks concepts required to identify and evaluate hazardous errors, especially within the context of AI. This paper introduces two key contributions to bridge this gap.

This paper seeks to identify the refrigeration load of a hybrid electric truck in order to find the demand power required by the energy management system. To meet this objective, in addition to the power consumption of the refrigerator, the vehicle mass needs to be estimated. The Recursive Least Squares (RLS) method with forgetting factors is applied for this estimation. As an example of the application of this parameter identification, the estimated parameters are fed to the energy control strategy of a parallel hybrid truck. The control system calculates the demand power at each instant based on estimated parameters. Then, it decides how much power should be provided by available energy sources to minimize the total energy consumption. The simulation results show that the parameter identification can estimate the vehicle mass and refrigeration load very well which is led to have fairly accurate power demand prediction.

Recently, the development of braking assistance system has largely benefit the safety of both driver and pedestrians. A robust prediction and detection of driver braking intention will enable driving assistance system response to traffic situation correctly and improve the driving experience of intelligent vehicles. In this paper, two types unsupervised clustering methods are used to build a driver braking intention predictor. Unsupervised machine learning algorithms has been widely used in clustering and pattern mining in previous researches. The proposed unsupervised learning algorithms can accurately recognize the braking maneuver based on vehicle data captured with CAN bus. The braking maneuver along with other driving maneuvers such as normal driving will be clustered and the results from different algorithms which are K-means and Gaussian mixture model (GMM) will be compared.

This paper discusses modeling of a power-split hybrid electric vehicle and the design of a longitudinal dynamics controller for the University of Waterloo’s self-driving vehicle project. The powertrain of Waterloo’s vehicle platform, a Lincoln MKZ Hybrid, is controlled only by accelerator pedal actuation. The vehicle’s power management strategy cannot be altered, so a novel approach to grey-box modeling of the OEM powertrain control architecture and dynamics was developed. The model uses a system of multiple neural networks to mimic the response of the vehicle’s torque control module and estimate the distribution of torque between the powertrain’s internal combustion engine and electric motors. The vehicle’s power-split drivetrain and longitudinal dynamics were modeled in MapleSim, a modeling and simulation software, using a physics-based analytical approach.

Battery packs of electric vehicles are typically composed of lithium-ion batteries with aluminum and copper acting as cell terminals. These terminals are joined together in series by means of connector tabs to produce sufficient power and energy output. Such critical electrical and structural cell terminal connections involve several challenges when joining thin, highly reflective and dissimilar materials with widely differing thermo-mechanical properties. This may involve potential deformation during the joining process and the formation of brittle intermetallic compounds that reduce conductivity and deteriorate mechanical properties. Among various joining techniques, laser welding has demonstrated significant advantages, including the capability to produce joints with low electrical contact resistance and high mechanical strength, along with high precision required for delicate materials like aluminum and copper.

It is imperative to have accurate tire models when trying to control the trajectory of a vehicle. With the emergence of autonomous vehicles, it is more important than ever before to have models that predict how the vehicle will operate in any situation. Many different types of tire models have been developed and validated, including physics-based models such as brush models, black box models, finite element-based models, and empirical models driven by data such as the Magic Formula model. The latter is widely acknowledged to be one of the most accurate tire models available; however, collecting data for this model is not an easy task. Collecting data is often accomplished through rigorous testing in a dedicated facility. This is a long and expensive procedure which generally destroys many tires before a comprehensive data set is acquired. Using a Vehicle Measurement System (VMS), tires can be modeled through on-road data alone.

In a conventional vehicle, the longitudinal shocks caused by the road obstacles cannot be effectively absorbed due to the fact that the longitudinal connections between the chassis and wheels are typically very stiff compared with the vertical strut where the regular spring is mounted. To overcome this limitation, a concept design of a planar suspension system (PSS) is proposed. The rather stiff longitudinal linkages are replaced by a spring-damping strut in a PSS so that the vibration along any direction in the wheel plane can be effectively isolated. For a vehicle with such suspension systems, the wheels can move forth and back with respect to the chassis. The wheelbase and load distribution at the front and rear wheels can change as a consequence of the implementation of the PSS on a vehicle. The planar system can induce changes in the vehicle dynamic behavior. This paper presents the overview introduction of a dynamic study of a vehicle with such suspension systems.

Modern electrified vehicles rely on drivers to manually adjust control parameters to modify the vehicle's powertrain, such as regenerative braking strength selection or drive mode selection. However, this reliance on infrequent driver input may lead to a mismatch between the selected powertrain control modifiers and the true driving environment. It is therefore advantageous for an electric vehicle's powertrain controller to make online identifications of the current driving conditions. This paper proposes an online driving condition identification scheme that labels drive cycle intervals collected in real-time based on a clustering model, with the objective of informing adaptive powertrain control strategies. HDBSCAN and K-means clustering models are fitted to a data set of drive cycle intervals representing a full range of characteristic driving conditions.

With the widespread development of automated driving systems (ADS), it is imperative that standardized testing methodologies be developed to assure safety and functionality. Scenario testing evaluates the behavior of an ADS-equipped subject vehicle (SV) in predefined driving scenarios. This paper compares four modes of performing such tests: closed-course testing with real actors, closed-course testing with surrogate actors, simulation testing, and closed-course testing with mixed reality. In a collaboration between the Waterloo Intelligent Systems Engineering (WISE) Lab and AAA, six automated driving scenario tests were executed on a closed course, in simulation, and in mixed reality. These tests involved the University of Waterloo’s automated vehicle, dubbed the “UW Moose”, as the SV, as well as pedestrians, other vehicles, and road debris.

In this paper, initial results of Li-ion battery performance characterization through field tests are presented. A fully electrified Ford Escape that is equipped by three Li-ion battery packs (LiFeMnPO4) including an overall 20 modules in series is employed. The vehicle is in daily operation and data of driving including the powertrain and drive cycles as well as the charging data are being transferred through CAN bus to a data logger installed in the vehicle. A model of the vehicle is developed in the Powertrain System Analysis Toolkit (PSAT) software based on the available technical specification of the vehicle components. In this model, a simple resistive element in series with a voltage source represents the battery. Battery open circuit voltage (OCV) and internal resistance in charge and discharge mode are estimated as a function of the state of charge (SOC) from the collected test data.

System identification is an important aspect in model-based control design which is proven to be a cost-effective and time saving approach to improve the performance of hybrid electric vehicles (HEVs). This study focuses on modeling and parameter estimation of the longitudinal vehicle dynamics for Toyota Prius Plug-in Hybrid (PHEV) with power-split architecture. This model is needed to develop and evaluate various controllers, such as energy management system, adaptive cruise control, traction and driveline oscillation control. Particular emphasis is given to the driveline oscillations caused due to low damping present in PHEVs by incorporating flexibility in the half shaft and time lag in the tire model.

Hybrid electric vehicles (HEVs) are considered as the most commercial prospects new energy vehicles. Most HEVs have adopted Atkinson cycle engine as the main drive power. Atkinson cycle engine uses late intake valve closing (LIVC) to reduce pumping losses and compression work in part load operation. It can transform more heat energy to mechanical energy, improve engine thermal efficiency and decrease fuel consumption. In this paper, the investigations of Atkinson cycle converted from conventional Otto cycle gasoline engine have been carried out. First of all, high geometry compression ratio (CR) has been optimized through piston redesign from 10.5 to 13 in order to overcome the intrinsic drawback of Atkinson cycle in that combustion performance deteriorates due to the decline in the effective CR. Then, both intake and exhaust cam profile have been redesigned to meet the requirements of Atkinson cycle engine.

Today, commercially available drones have limited use-cases in the rapidly evolving community. However, with advances in drone and software technology, it is possible to utilize these aerial machines to solve problems in a variety of industries such as mining, medical, construction, and law enforcement. For example, in order to reduce time of investigation, Indiana State Police are currently utilizing ad-hoc commercial drones to reconstruct crash scenes for insurance and legal purposes. In this paper, we illustrate how to effectively integrate drones for in-vehicle services and real-time prediction for automotive applications. In order to accomplish this, we first integrate simpler controls such as voice-commands to control the drone from the vehicle. Next, we build smart prediction software that monitors vehicle behavior and reacts in real-time to collisions.

An electric vehicle model has been developed with four direct-drive in-wheel motors. A high-level vehicle stability controller is proposed, which uses the principles of fuzzy logic to determine the corrective yaw moment required to minimize the vehicle sideslip and yaw rate errors. A genetic algorithm has been used to optimize the parameters of the fuzzy controller. The performance of the controller is evaluated as the vehicle is driven through a double-lane-change maneuver. Preliminary results indicate that the proposed control system has the ability to improve the performance of the vehicle considerably.

A compact sized vehicle that has a narrow track could solve problems caused by vehicle congestion and limited parking spaces in a mega city. Having a smaller footprint reduces the vehicle's total weight which would decrease overall vehicle power consumption. Also a smaller and narrower vehicle could travel easily through tight and congested roads that would speed up the traffic flow and hence decrease the overall traffic volume in urban areas. As an additional benefit of having a narrow track length, a driver can experience similar motorcycle riding experience without worrying about bad weather conditions since a driver sits in a weather protected cabin. However, reducing the vehicle's track causes instability in vehicle dynamics, which leads to higher possibility of rollovers if the vehicle is not controlled properly. A three wheel personal vehicle with an active tilting system is designed in MapleSim.