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Digital mapping tools have become indispensable for road navigation. Applications like Waze and Google Maps harness the power of satellite imagery to provide precise visualization of GPS coordinates. The field advanced significantly in May 2023 with the introduction of dynamic 3D representations of the Earth. Companies such as Cesium now offer Unity3D and Unreal Engine Application Programming Interface that can be applied to geospatial applications. These images are no longer static and offer the opportunity to provide seamless continuous navigation. Driving simulation has been widely used for training and research. We investigate with this project the potential of this new geospatial database as a tool for scenario development to study manual and autonomous driving. We present an in-vehicle driving simulation integration that employs a real steering wheel and pedals from a stationary vehicle as controls.

Lexan 500 is a new polycarbonate grade that was developed to meet market needs for a material combining the strength and durability of metals with lighter weight, design flexibilities, and processing economics of plastics. Lexan 500 has a high impact strength, an excellent dimensional stability, a low coefficient of thermal expansion, and a low water absorption rate. Lexan 500 can be used as a metal replacement in areas where designers can take advantage of the benefits of a structurally rigid thermoplastic without sacrificing impact strength, with added advantages of reduced processing costs and elimination of costly secondary finishing and assembly operations. Lexan 500 is particularly suited to applications requiring high dimensional stability and the ability to mold easily to close tolerances while maintaining excellent surface appearance.

This paper outlines the characterization of a Li-Ion Iron Phosphate battery pack with nominal voltage of 700V as well as the modeling of this pack as an equivalent electrical circuit (EEC) for the purpose of vehicle simulations. For a higher level of fidelity and accuracy, the equivalent circuit is initially modeled as an R-2RC circuit which consists of a voltage source with one resistor (R) and two resistor-capacitor (RC) branches. In this modeling effort, first, several open circuit voltage (OCV) determination methods in the literature are benchmarked and state-of-charge (SOC) dependent OCV curve which is used in the voltage source of the EEC model is derived. Then, two methods of parameter estimation of the EEC are developed for both step current and dynamic current profiles. The first estimation method is applicable to discharge or charge step currents and relies mostly on the relaxation portion of the battery response and involves some manual calibration.

Due to their high energy density, power density, and durability, lithium-ion (Li-ion) batteries are rapidly becoming the most popular energy storage method for electric vehicles. Difficulty arises in accurately estimating the amount of left capacity in the battery during operation time, commonly known as battery state of charge (SOC). This paper presents a comparative study between six different Equivalent Circuit Li-ion battery models and two different state of charge (SOC) estimation strategies. The Battery models cover the state-of-the-art of Equivalent Circuit models discussed in literature. The Li-ion battery SOC is estimated using non-linear estimation strategies i.e. Extended Kalman filter (EKF) and the Smooth Variable Structure Filter (SVSF). The models and the state of charge estimation strategies are compared against simulation data obtained from AVL CRUISE software.

Hybrid, plug-in hybrid, and electric vehicles have enthusiastically embraced rechargeable Li-ion batteries as their primary/supplemental power source of choice. Because the state of charge (SoC) of a battery indicates available remaining energy, the battery management system of these vehicles must estimate the SoC accurately. To estimate the SoC of Li-ion batteries, we derive a normalized state-space model based on Li-ion electrochemistry and apply a Bayesian algorithm. The Bayesian algorithm is obtained by modifying Potter's squareroot filter and named the Potter SoC tracker (PST) in this paper. We test the PST in challenging test cases including high-rate charge/discharge cycles with outlier cell voltage measurements. The simulation results reveal that the PST can estimate the SoC with accuracy above 95% without experiencing divergence.

The performance of a High Voltage (HV) battery in an advanced propulsion application is often highly dependent on the customer controlled climate settings of the vehicle system. Cooling of the battery requires an understanding of the environment in which it is expected to operate. Results from testing on various air-cooled systems will be discussed to portray the interactions of the battery system design and the climate control system design. The following topics (in relation to battery cooling) will be discussed: climate control system temperature settings; climate-control system fan settings; climate control system recirculation mode and outside air mode settings; venting of the battery thermal system exhaust air; and the battery location and mounting.

Li-alloy/FeS battery designs, based upon a well-characterized 300-Ah cell developed by Westinghouse Oceanic Division, have been developed for four electric vans currently under development by the U.S. Department of Energy and the Electric Power Research Institute. Computerized cell models were developed to calculate power, energy, weight, and volume values for a cell while varying key design parameters. Battery specifications and vehicle performance are given for the Chrysler TE Van, GMC G-Van, Ford ETX-II, and the Eaton DSEP.

People through many years of experience, have developed a great intuitive sense for navigation and spatial awareness. With this intuition people are able to apply a near rules based approach to their driving. With a transition to autonomous driving, these intuitive skills need to be taught to the system which makes perception is the most fundamental and critical task. One of the major challenges for autonomous vehicles is accurately knowing the position of the vehicle relative to the world frame. Currently, this is achieved by utilizing expensive sensors such as a differential GPS which provides centimeter accuracy, or by using computationally taxing algorithms to attempt to match live input data from LiDARs or cameras to previously recorded data or maps. Within this paper an algorithm and accompanying hardware stack is proposed to reduce the computational load on the localization of the robot relative to a prior map.

In the world of automated driving, sensing accuracy is of the utmost importance, and proving that your sensors can do the job is serious business. This is where ground-truth labeling has an important role in Autoliv’s validation process. Currently, annotating ground-truth data is a tedious and manual effort, involving finding the important events of interest and using the human eye to determine objects from LiDAR point cloud images. We present a workflow we developed in MATLAB to alleviate some of the pains associated with labeling point cloud data from a LiDAR sensor and the advantages that the workflow provides to the labeler. We discuss the capabilities of a tool we developed to assist users in visualizing, navigating, and annotating objects in point cloud data, tracking these objects through time over multiple frames, and then using the labeled data for developing machine learning based classifiers.

This paper presents a proof of concept for an algorithm to determine the attitude of a multi-layer laser rangefinder or LiDAR (Light Detection And Ranging) relative to a reference frame given the ability of the LiDAR to make measurements to a planar surface with sufficient and proper excitation. Due to the highly non-linear nature of the problem presented, weighted recursive least squares along with a high order unscented transform are used to obtain a final result which is capable of being extended to vehicle safety systems to provide a measurement of the roll and pitch of a vehicle relative to the road's surface with a high degree of accuracy.

LiDAR sensors have played more and more important role on Intelligent and Connected Vehicles (ICV) and Advanced Driver Assistance Systems (ADAS) .However, the development and testing of LiDAR sensors under real driving environment for ADAS applications are greatly limited by various factors, and often are impossible due to safety concerns. This paper proposed a novel functional LiDAR model under virtual driving environment to support development of LiDAR-based ADAS applications under early stage. Unlike traditional approaches on LiDAR sensor modeling, the proposed method includes both geometrical modeling approach and physical modeling approach. While geometric model mainly produces ideal scanning results based on computer graphics, the physical model further brings physical influences on top of the geometric model. The range detection is derived and optimized based on its physical detection and measurement mechanism.

Although SAE level 5 autonomous vehicles are not yet commercially available, they will need to be the most intelligent, secure, and safe autonomous vehicles with the highest level of automation. The vehicle will be able to drive itself in all lighting and weather conditions, at all times of the day, on all types of roads and in any traffic scenario. The human intervention in level 5 vehicles will be limited to passenger voice commands, which means level 5 autonomous vehicles need to be safe and capable of recovering fail operational with no intervention from the driver to guarantee the maximum safety for the passengers. In this paper a LiDAR-based fail-safe emergency maneuver system is proposed to be implemented in the level 5 autonomous vehicle.

The accuracy of parking slot detection is a challenge for the safety of the Automated Valet Parking (AVP), while traditional methods of range sensor-based parking slot detection have mostly focused on the detection rate in a scenario, where the ego-vehicle must pass by the slot. This paper uses three-dimensional Light Detection And Ranging (3D LiDAR) to efficiently search parking slots around without passing by them and highlights the accuracy of detecting and tracking. For this purpose, a universal process of 3D LiDAR-based high-accuracy slot perception is proposed in this paper. First, the method Minimum Spanning Tree (MST) is applied to sort obstacles, and Separating Axis Theorem (SAT) are applied to the bounding boxes of obstacles in the bird’s-eye view, to find a free space between two adjacent obstacles. These bounding boxes are obtained by using common point cloud processing methods.

Advanced Driver Assistance Systems (ADAS) enable safer driving by relying on the inputs from various sensors including Radar, Camera, and LiDAR. One of the newly emerging ADAS features is Predictive Cruise Control (PCC). PCC aims to optimize the vehicle’s speed profile and fuel efficiency. This paper presents a novel approach of using the point cloud of a LiDAR sensor to develop a PCC feature. The raw point cloud is utilized to detect objects in the surrounding environment of the vehicle, estimate the grade of the road, and plan the route in drivable areas. This information is critical for the PCC to define the optimal speed profile of the vehicle while following the planned path. This paper also discusses the developed algorithms of the LiDAR data processing and PCC controller. These algorithms were tested on FEV’s Smart Vehicle Demonstrator platform.

The technological advancements of Advanced Driver Assistance Systems (ADAS) sensors enable the ability to; achieve autonomous vehicle platooning, increase the capacity of road lanes, and reduce traffic. This article focuses on developing urban autonomous platooning using LiDAR and GPS/IMU sensors in a simulation environment. Gazebo simulation is utilized to simulate the sensors, vehicles, and testing environment. Two vehicles are used in this study; a Lead vehicle that follows a preplanned trajectory, while the remaining vehicle (Follower) uses the LiDAR object detection and tracking information to mimic the Lead vehicle. The LiDAR object detection is handled in multiple stages: point cloud frame transformation, filtering and down-sampling, ground segmentation, and clustering. The tracking algorithm uses the clustering information to provide position and velocity of the Lead vehicle which allows for vehicle platooning.

Intelligent Vehicle-Highway Systems (IVHS) may change the character of automobile accidents and of automobile accident lawsuits in the United States. Successful implementation of IVHS technology will require significant capital investment by manufacturers and highway departments. Investment may be deterred by concern about potential legal liability and the cost of liability insurance. Liability and insurance experience with other pathbreaking technologies -- commercial air transportation, nuclear power, and satellites -- suggests that the liability risks of advanced stages of IVHS technology should be managed through federal or state legislation.

A research crash simulator is described which is located at the Liberty Mutual Research Center in Hopkinton, Massachusetts. A seven-foot sled is accelerated by compressed air at speeds up to 60 mph, and then decelerated by a combination of aluminum honeycomb and a hydraulic energy absorber. Normally the sled carries an automotive seat, with some type of restraint system, and an anthropometric dummy. Information concerning collision events in the simulator is obtained from electronic transducers and high speed motion pictures. A number of car occupant protection systems have been tested on this simulator, and typical data are presented to illustrate its performance.

Licensing technology has long been established a viable means for helping both parties of such an agreement improve their technology bases. The licensor/licensee relationship must be close and interactive to optimize these improvements. The importance of the selection process of potential licensees as well as the structuring of how the technology is transferred are key items for the success of license agreements. Future licensing activities may require tighter controls on technology transfers to protect against the creation of new competitors.

High-precision and real-time ego-motion estimation is vital for autonomous vehicle. There is a lot GPS-denied maneuver such as underground parking lot in urban areas. Therefore, the localization system relying solely on GPS cannot meets the requirements. Recently, lidar odometry and visual odometry have been introduced into localization systems to overcome the problem of missing GPS signals. Compared with visual odometry, lidar odometry is not susceptible to light, which is widely applied in weak-light environments. Besides, the autonomous parking is highly dependent on the geometric information around the vehicle, which makes building map of surroundings essential for autonomous vehicle. We propose a lidar inertial odometry and mapping. By sensor fusion, we compensate for the drawback of applying a single sensor, allowing the system to provide a more accurate estimate.

Recently, outboard engine technology has advanced significantly. With these new technologies comes a substantial improvement in emissions compared to traditional carbureted two-stroke engines. Some two-stroke gasoline direct injection (GDI) marine outboard engines are now capable of meeting California Air Resources Board 2008 Ultra-Low emissions standards. With improvement of gaseous emissions, studies are now being conducted to assess particulate matter (PM) emissions from all new technology marine outboard engines which include both four-stroke and two-stroke designs. Methods are currently being developed to determine the best way to measure PM from outboard engines. This study assesses gaseous and PM emissions, mutagenic activity of PM and oil consumption of two different technologies over the useful life of the engines.