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Expanding various future motilities such as PBV(Purpose Built Vehicle), UAM(Urban Air Mobility), and Robo-taxi, the application of autonomous driving system (ADS) technology is also spreading. In order to prepare for the disruption of autonomous vehicle operation or the occurrence of an accident, and to secure customer safety, important is monitoring the anomaly of a vehicle. In this study, the process to diagnose the anomaly and to evaluate the remaining useful life of chassis system of the vehicle under autonomous driving conditions is established. Once an autonomous driving simulation model of the vehicle with Modelica is constructed, a lane keeping assistant (LKA) system with Matlab is combined with the model to perform the driving simulations. Applying the degrading data of three kinds of chassis systems (a shock absorber damper, a suspension bush, and a tire), the deteriorated LKA behavior is monitored with K-NN (K-nearest neighbor) and GMM (Gaussian Mixture Model).

The advancement of Advanced Driver Assistance System (ADAS) technologies offers tremendous benefits. ADAS features such as emergency braking, blind-spot monitoring, lane departure warning, and adaptive cruise control, etc., are promising to lower on-road accident rates and severity. With a common goal for the automotive industry to achieve higher levels of autonomy, maintaining ADAS sensor performance and reliability is critical to ensure adequate ADAS functionality. Currently, the challenges faced by ADAS sensors include performance degradation in adverse weather conditions and a lack of controlled evaluation methods. Outdoor testing encounters repeatability issues, while indoor testing lacks realistic conditions. This study proposes a hybrid method to combine the advantages of both outdoor and indoor testing approaches in a Drive-thru Climate Tunnel (DCT).

This paper describes a method of walking assistant for pedestrian. With the development of motorization, pedestrian protection techniques become more and more important. Advanced driving assistance systems (ADAS) are improved rapidly for more safety. However, the risk of pedestrians encountering traffic accidents remains high. Therefore, it is necessary to develop an advanced walking assistant system for pedestrian protection. Previous paper proposed a new method for road crossing assistance. this method that has image recognition system based on the combination of convolute neural network and computational visualization technology, is efficient for assistant for pedestrian to recognize pedestrian signals and crosswalk marks. However, it was necessary for the device’s camera to capture the traffic light when the pedestrian was stopped before the crossing.

In order to validate the operation of advanced driver assistance systems (ADAS), tests must be performed that assess the performance of the system in response to different scenarios. Some of these systems are designed for crash-imminent situations, and safely testing them requires large stretches of controlled pavement, expensive surrogate targets, and a fully functional vehicle. As a possible more-manageable alternative to testing the full vehicle in these situations, this study sought to explore whether tests could be performed with isolated sub-systems on a bench via a hardware-in-the-loop methodology. For camera systems, these benches are called Camera-in-the-Loop (CiL) systems and involve presenting visual stimuli to the device via an external input. In this work, the Mobileye 630 aftermarket monocular camera system was tested across three environments – track based (real life) testing, GoPro CiL bench (recorded real life) testing, and Prescan CiL bench (simulated life) testing.

Testing vision-based advanced driver assistance systems (ADAS) in a Camera-in-the-Loop (CiL) bench setup, where external visual inputs are used to stimulate the system, provides an opportunity to experiment with a wide variety of test scenarios, different types of vehicle actors, vulnerable road users, and weather conditions that may be difficult to replicate in the real world. In addition, once the CiL bench is setup and operating, experiments can be performed in less time when compared to track testing alternatives. In order to better quantify normal operating zones, track testing results were used to identify behavior corridors via a statistical methodology. After determining normal operational variability via track testing of baseline stationary surrogate vehicle and pedestrian scenarios, these operating zones were applied to screen-based testing in a CiL test setup to determine particularly challenging scenarios which might benefit from replication in a track testing environment.

Contemporary ADS and ADAS localization technology utilizes real-time perception sensors such as visible light cameras, radar sensors, and lidar sensors, greatly improving transportation safety in sufficiently clear environmental conditions. However, when lane lines are completely occluded, such as in inclement weather scenarios, the reliability of on-board automated perception systems breaks down, and vehicle control must be returned to the human driver. High-definition map data, which contains a high level of detail about road features, is an alternative source of the required lane line information. This study details a novel method where high-definition map data are processed to locate fully occluded lane lines, allowing for automated path planning. A proxy high-definition map data set with high-accuracy geospatial features was generated for routes at both the Eaton Proving Grounds and Parkview Campus at Western Michigan University (WMU).

Blind Spot is a region around the vehicle which is not directly visible to the driver either due to obstruction or due to limitation of field of view. Based on the seating position and mirror alignments a certain portion behind the car is completely invisible to the driver. Vehicles travelling in this area are under major threat since their presence is completely unknown to the preceding vehicles. Any lane shift maneuvers carried out by the preceding vehicles leads to collision. This region is asymmetric and is different on both the sides because of the driver’ seating position and mirror orientation. According to NHTSA report major car crashes occurs when there is a vehicle present in the blind spot during lane change. Several remedies like wide angle mirrors, alarming systems etc. have been introduced but, in most cases, accidents occur due to driver’s negligence.

Due to the increasing complexities, the safety assurances for automated driving systems (ADSs) and Advanced Driver Assistance Systems (ADASs) pose challenges. Recent development within the industry and academia suggests a scenario-based approach underpinned by the system’s operational design domain (ODD) for its safety assurance. In such framework, the ODD defines the safe operating boundary, whereas the scenarios set out individual test conditions. To assess the behaviour of the system, a critical element for road safety is the ability to respect the rules of the road. This paper joins together ODDs, scenarios, and rules of the road to form a scalable ODD-based safety assurance framework. The backbone of the framework contains a coherent and common taxonomy to describe the ODDs and behaviour competencies, the scenario tagging structure from the ASAM OpenLABEL standard has been used in the example use cases.

To prevent traffic accident, Autonomous Driving (AD) / Advanced Driver Assistance System (ADAS) has been actively developed. As the role of AD/ADAS increasing, the number of its test scenario is increasing year by year. While huge number of test is required, we have limits of real vehicle test alone, and simulation test is necessary.Test scenarios are defined officialy, such as the collision scenario and the traffic disturbance scenario with combination of relative position and vehicle movement. In order to guarantee the safety of product, it is important to be tested with these scenarios. Thus we constructed Model In the Loop Simualtion (MILS) environment to make efficient for AD/ADAS test process. But the manual operation of creating test scenario was an issue. This paper proposes automatic scenario creation technology that satisfies a collision constraint.

Vehicle safety enhancements to protect human lives accident the event of accidents has been the prime focus for the automakers all along. The safety goals associated with savings lives led to innovation relative to passive safety features in a car ranging from development of vehicles structure to absorb energy during a crash event. Every vehicle must meet the frontal, side, rear, and roof crush impact tests. Typically, the human body limit is from 20g to 40 g of acceleration and the vehicle structure should be designed to absorb the kinetic energy by the vehicle structure deformation. The B-pillar has crush requirements and side airbag curtains are designed to protect the occupant from head, neck, and shoulder injuries. The requirements cover FMVSS ranging from 201 to 216. The vehicle structural design is further assisted by the passive safety devices such as seat belts, air bags, energy absorbing steering column and side curtains to protect the occupant in a frontal crash.

Advanced driver assistance systems rely on external sensors that encompass the vehicle. The reliability of such systems can be compromised by adverse weather, with performance hindered by both direct impingement on sensors and spray suspended between the vehicle and potential obstacles. The transportation of road spray is known to be unsteady phenomenon, driven by the turbulent structures that characterise automotive flow fields. Further understanding of this unsteadiness is a key aspect in the development of robust sensor implementations. This paper outlines an experimental method used to analyse the spray ejected by an automotive body, presented through a study of a simplified vehicle model with interchangeable rear-end geometries. Particles are illuminated by laser light sheets as they pass through measurement planes downstream of the vehicle, facilitating the imaging of the instantaneous structure of the spray.

DEXT (Diagnostic Extract Template) is a standard diagnostic data format specified in AUTOSAR for UDS and fault memory, with extensions for WWH-OBD, and FIM (AUTOSAR 4.3.0). DEXT covers the configuration of all basic software modules for diagnostics software that are supported in AUTOSAR. It is possible to describe not only data transported using respective protocols but also the origin of data in ECU's application software. While in another diagnostic format like ODX, the source of data in the ECU is not specified. On one hand, the information gap between ODX and ECU software is more evident when it comes to the description of the fault memory, on the other hand, the AUTOSAR-ECUC format changes frequently and is adapted with every new version of the standard and it supports manufacturer-specific extensions, thus making it unsuitable as a generic exchange format.

ADAS (Advanced Driver Assistance System) functions can help the driver avoid accidents or mitigate their effect when they occur, and are pre-cursors to full autonomous driving (SAE defined as Level 4+). The main goal of this work is to develop a model-based system to actuate the Evasive Maneuver Assist (EMA) function. A typical scenario is the situation where longitudinal Autonomous Emergency Braking (AEB) is too late and the driver has to adopt evasive maneuver to avoid an object suddenly appearing on the road ahead. At this time, EMA can help improve the driver’s steering and braking operation in a co-ordinated way. The vehicle maneuverability and response performance will be enhanced when the driver is facing the collision. The function will additionally let the vehicle steer in a predetermined optimized trajectory based on a yaw rate set point and stabilize the vehicle. The EMA function is introduced with some analysis of benchmarking data.

Accurate tire pressure monitoring system (TPMS) is of great practical importance, as many traffic accidents are caused by abnormal tire pressure and temperature. Although the more maturely developed direct TPMS is widely used commercially, the reliability and safety of its power supply module has great concern. The piezoelectric-based SAW sensor is considered to have great potential in this field because of its passive, wireless and small size advantages. This paper presents the application of passive and wireless SAW sensors for real-time tire condition monitoring, and proposes a fast detection method with high sampling rate. Firstly, a three dimensional model of the SAW sensor is developed in this paper combing finite element model and the perturbation method for simulating the sensitivity and response. Secondly, a SAW chip with three resonators is designed and the package structure is optimized.

The presented study is dedicated to the technology supporting the vehicle state estimation and motion control with a concept drone, which helps the vehicle in sensing the surrounding and driving conditions. This concept allows also extending the functionality of the sensors mounted on the vehicle by replacing or including additional parameter observation channels. The paper discusses the feasibility of such a drone-vehicle-interaction as well as demonstrates several design configurations. In this regard, this paper presents a general description of the proposed drone system that assists the vehicle and describes an experiment in measuring the profile of the road with a range sensor. The results obtained in the experiment are described in terms of the accuracy that we can obtain using the drone and are compared with other studies that use the methods of estimation from the sensors mounted on the vehicle.

High-speed vehicles in low illumination environments severely blur the images used in object detectors, which poses a potential threat to object-detector-based advanced driver assistance systems (ADAS) and autonomous driving algorithms. Augmenting the training images for object detectors is an efficient way to mitigate the threat from motion blur. However, little attention has been paid to the impact of vehicle moving status and the position of objects in the traffic scene. In this paper, we present a vehicle kinematics-based image augmentation algorithm by modeling and analyzing the traffic scenes and thus achieve higher robustness improvement on object detectors against motion blur. Firstly, we propose a traffic scene model considering vehicle motion and the relationship between the vehicle and the object in the traffic scene. Simulations based on typical ADAS test scenes show that the high vehicle speed and close object distance is the key factor in generating motion blur.

Efficiency in energy consumption of an electric vehicle (EV) has a significant value to both the vehicle manufacturer and the owner due to the cost of energy and limitations of the DC motor and battery compared to internal combustion engines. Nowadays, with the development of driver-aid systems in most vehicles, such as adaptive cruise control (ACC) or cooperative adaptive cruise control (CACC), drivers have a much safer driving experience. While safety is a significant factor to consider, there are different factors to add to this optimization problem, as power consumption optimality is one of them. This paper introduces an energy-optimized ACC platform with a prediction horizon for the speed profile of the host vehicle. This paper focuses on optimization in longitudinal tracks consisting of ten different drive-cycles in several driving scenarios, such as highways, urban areas, and test tracks with multiple stops.

With the proliferation of ADAS and autonomous systems, the quality and quantity of the data to be used by vehicles has become crucial. In-vehicle sensors are evolving, but their usability is limited to their field of view and detection distance. V2X communication systems solve these issues by creating a cooperative perception domain amongst road users and the infrastructure via communicating accurate, real-time information.  In this paper, we propose a novel Consolidated Object Data Service (CODS) for multi-RAT V2X communication. This service collects information using BSM packets from the vehicular network and perception information from infrastructure-based sensors. The service then fuses the collected data, offering the communication participants with a consolidated, deduplicated, and accurate object database. Since fusing the objects is resource intensive, this service can save in-vehicle computation costs.

Vehicular odometer serve as a standard component in driver assistance system to provide continuous navigation. Odometer fraud is the disconnection, resetting, or alteration of a vehicle’s odometer with the intent to change the number of miles indicated. Odometer fraud occurs when the seller of a vehicle falsely represents the actual mileage of a vehicle to the buyer. But the Odometer readings are essential when it comes to ascertaining the fair market value of a used vehicle. Hence, there is a need to protect the odometer which resides in the instrument cluster of the digital cockpit. Any manipulation is very difficult to detect and to prove once made, even by expert technicians using specific On-Board Diagnostics (OBD) testing devices. One of the most critical issues is that currently odometers are not locked out from external access, in contrast to other vehicle components, which have higher protection levels.

Modern vehicles use automated driving assistance systems (ADAS) products to automate certain aspects of driving which improves operational safety. In the U.S. in 2020, 38,824 fatalities occured due to automotive accidents, and typically about 25% of these are associated with inclement weather. ADAS features have been shown to reduce potential collisions by up to 21%, thus reducing overall accidents. But ADAS typically utilize camera sensors that rely on lane visibility and the absence of obstructions in order to function, rendering them ineffective in inclement weather. To address this research gap, we propose a new technique to estimate snow coverage so that existing and new ADAS features can be used during inclement weather. In this study, we use a single camera sensor and historical weather data to estimate snow coverage on the road. Camera data was collected over 6 miles of arterial roadways in Kalamazoo, MI.