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The glare to pedestrians remains one of the biggest challenges since the invention of the automobile and the automobile lighting. As far as pedestrians are concerned, whether the roadside pedestrians or the pedestrians in the crossing area are almost like lambs to the slaughter. To balance the glare interference of automobile lighting and protecting pedestrians, modeling and simulation for the pedestrian glare have been theorized by the use of forest sunlight effects and a novel controlled dark photonic channel. Based on the modeling and simulation, a novel full-factors light environment control system and pedestrian glare-free lighting system have been derived.

As new technology is added to vehicles and traffic congestion increases, there is a concern that drivers will be overloaded. As a result, there has been considerable interest in measuring driver workload. This can be achieved using many methods, with subjective assessments such as the NASA Task Loading Index (TLX) being most popular. Unfortunately, the TLX is unanchored, so there is no way to compare TLX values between studies, thus limiting the value of those evaluations. In response, a method was created to anchor overall workload ratings. To develop this method, 24 subjects rated the workload of clips of forward scenes collected while driving on rural, urban, and limited-access roads in relation to 2 looped anchor clips. Those clips corresponded to Level of Service (LOS) A and E (light and heavy traffic) and were assigned values of 2 and 6 respectively.

In this paper, a reinforcement learning-based method is proposed to adapt autonomous vehicle passengers’ expectation of comfort through in-situ human-vehicle interaction. Ride comfort has a significant influence on the user’s experience and thus acceptance of autonomous vehicles. There is plenty of research about the motion planning and control of autonomous vehicles. However, limited studies have explicitly considered the comfort of passengers in autonomous vehicles. This paper studies the comfort of humans in autonomous vehicles longitudinal autonomous driving. The paper models and then improves passengers’ feelings about autonomous driving behaviors. This proposed approach builds a control and adaptation strategy based on reinforcement learning using human’s in-situ feedback on autonomous driving. It also proposes an adaptation of humans to autonomous vehicles to account for improper human driving expectations.

This paper covers research findings on digital projections on the road. Data is provided for the root cause analysis of non-existing distraction proven by several studies. The study describes if and in which geometrical space road projections are visible to other road traffic participants. Such participants can be e.g. oncoming, passing drivers or pedestrians standing aside the road. The paper data shows where projections are recognizable and assignable to the original intention of the projection. A grid was created to identify the areas where digital projections could be understood and where the digital projections were just illegible. A dominant factor is the grazing incidence. The photons are distributed over a larger area and only the driver’s view makes a virtual compression of the illuminated area in order to make the signals legible. The results show that distraction for other road participants is unlikely for any position outside very limited areas.

It has been shown that additional light signals are beneficial in the car 2 human communication. This study addresses detection, discomfort, brightness, recognition of intention and the perception of safety, of different symbols and dynamics used for communication. Splitted in two parts, the first use case is a lane crossing situation, where the car gives instructions to the pedestrian via an angular restricted external Human Machine Interface (eHMI) in the driver’s window. Results show that a symbol which blinks first and is then statically shown leads to fast and best detection. The intention of a red stop hand and green pedestrian is clearly understood. A combination of a near road-projection and the eHMI leads to confusion. An angle of 55° to 25° has been proven to be sufficient for displaying the information. In the second use case a cyclist is approaching the automated vehicle (AV) from behind and passes on a bicycle path.

Flashing emergency and warning lights are critical elements of public safety and traffic control during roadway incidents. These lights should not only alert drivers to their presence, but also should inform them of who and what is present on the scene, and should help to manage the responses of drivers as they navigate past the incident. First responder and driver safety depend upon all three of these functions, yet standards focus almost entirely on alerting drivers. A full-scale outdoor field study was carried out during daytime, during nighttime on dry pavement and during nighttime on wet pavement, using a mock-up roadside scene containing three police vehicles. The lights on the vehicles were adjusted to produce different levels of intensity, flash rate, and synchronization of lights across all three vehicles. In some cases, sequentially flashing lights were present.

A safe automated vehicle must “know when it doesn’t know.” Automated vehicles cannot depend on the traditional drive-fail-fix cycle due to heavy tail problem distributions supplying virtually infinite problems. In order to be safe, automated vehicles require the ability to handle unforeseen untested “unknown unknown” situations. Safety Performance Indicators (SPIs) at deep-enough sub-claim levels can uncover safety case claim violations in a ‘leading’ fashion - prior to safety events. This paper introduces Dynamic Realtime SPIs (SPIs calculated at runtime) at sufficiently low safety case claim levels which yield runtime recognition of safety case claim violations and can be used by the ADS to infer that it is encountering an “unknown unknown” situation. Then, because “knowing when an ADS doesn’t know” is insufficient to ensure AV safety, we introduce the Dynamic Realtime SPI (DRSPI) framework, for handling such occurrences.

As the deployment of automated vehicles (AVs) on public roadways expands, there is growing interest in establishing metrics that can be used to evaluate vehicle operational safety. The set of Operational Safety Assessment (OSA) metrics, that include several safety envelope-type metrics, previously proposed by the Institute of Automated Mobility (IAM) are a step towards this goal. The safety envelope OSA metrics can be computed using kinematics derived from video data captured by infrastructure-based cameras and thus do not require on-board sensor data or vehicle-to-infrastructure (V2I) connectivity, though either of the latter data sources could enhance kinematic data accuracy. However, the calculation of some metrics includes certain vehicle-specific parameters that must be assumed or estimated if they are not known a priori or communicated directly by the vehicle.

The improvement of road transport safety requires the development of advanced vehicle safety systems, whose development could be facilitated by using complex interaction models of different road users. To this end, this paper deals with the modeling of multi-vehicle/multi-pedestrian interactions at unsignalized crosswalks. This multi-agent modeling approach extends on the existing basic model covering only single-vehicle/single-pedestrian interactions. The basic model structure and parameters have remained the same, as it was previously experimentally calibrated and thoroughly verified. The proposed modeling procedure employs the basic model within the multi-agent setting based on its application to relevant single-vehicle and single-pedestrian pairs. The resulting, so-called pre-decisions are then used for making final crossing decisions in a current time step for each agent.

Seat Heater testing methods traditionally rely on a human subject to provide normal contact, load, and thermal conditions. This creates a thermal environment closer to what an actual customer might experience but it also introduces a variation from individual subject’s seating posture, body size, and metabolic differences. This paper describes the development and initial testing results of a passive, heated seat testing manikin (or HANIKIN) that is intended to replace human subjects for more meaningful, repeatable objective testing.

As automotive seating comfort is expected to see an upward trend, virtual comfort analysis is expected to pick up its importance and Body Pressure Distribution (BPD) is foreseen to be the flag bearer in this race towards seat comfort. The shift of focus from BPD testing to virtual comfort analysis has been a positive sign for market. This paper will discuss in detail several different human seating postures during BPD measurement and their influence on seat BPD with the help of virtual comfort analysis tool. This knowledge will be helpful to put more emphasis on several key aspects for capturing BPD maps during real BPD testing (This paper does not discuss any physical testing). When the real-time-tested BPD map would be produced repeatedly with less variation with the analytical BPD map, a comfortable seat design would be achieved in a shorter period in terms of production with greater accuracy

Based on the success of the second-generation Genesis G80 model, Hyundai Motor has declared the independence of Genesis as a luxury car brand in 2015. The third-generation G80 is the representative model of the Genesis brand and has a unique identity of Genesis that can surpass its competitors. In addition, it was necessary to develop seats that were considered not only for ICE but also for the scalability of electric vehicles. A newly formed Genesis organization established the Genesis design philosophy of its own. Four key elements of the design philosophy were comfort, aesthetics, usability and safety. The third-generation Genesis seats incorporate its design philosophy of seat design and new technologies based on comfort, aesthetics, usability, and safety. This paper describes the seat development of the Ergo Motion seat, Rear Seat Relaxtion(Relax + Position), Seat Syling, AVN switch display and PSS(pre-active safety seat )system, which are representative technologies.

The Insurance Institute of Highway Safety (IIHS) introduced driver side small overlap test in 2012 and added the passenger side small overlap test in 2018 to the top safety pick plus ratings requirement. The injury of a passenger’s outboard right foot in the passenger-side small overlap rigid barrier (PSORB) test is of more concern compared to the driver’s outboard left foot in the driver-side small overlap rigid barrier (DSORB) test. The reason is, the passenger’s right foot is positioned just above the carpet on the toe pan, and is closer to the barrier during the PSORB impact event, unlike the driver’s outboard left foot in DSORB, which rests on a stiff foot rest. So it is often necessary to develop countermeasures to protect the passenger from lower leg injuries.

Researches on pedestrian protection have become a very important theme in automotive industry. Design for vehicle front-bumper system has proven rather essential and been extensively used to improve the vehicle performance of pedestrian protection. However, there are some limitations in the design of vehicle front-bumper system to meet a multiple-pedestrian impact conditions at the same time. In order to improve the vehicle performance of lower extremity and pelvis protection for pedestrian, a new type of front bumper airbag was developed. Firstly, based on European New Car Assessment Programme (Euro-NCAP), the Flexible Pedestrian Legform Impactor (Flex-PLI) to vehicle and Upper Pedestrian Legform Impactor (U-PLI) to vehicle impact tests are carried out to evaluate the pedestrian protection performance of the initial structure.

In order to further reduce the pedestrian fatalities, the improvement of pedestrian safety performance of vehicles is needed. One of the way to further understand read-world pedestrian accidents is the evaluation by using a whole-body pedestrian dummy. In the past studies, the leg, the thigh and the pelvis of the pedestrian dummy were developed and improved. However, the requirements for the biofidelity of the pedestrian dummy have been improved in SAE J2782. Therefore, this study aimed to evaluate these responses of the past studies by using new requirements and to modify these parts that didn’t meet them. The force-defection curves from 3-point lateral bending tests for the leg and the thigh were compared with the corridors updated in SAE J2782. The biofidelity of the pelvis was evaluated in dynamic lateral compression tests of the isolated pelvis. The sacrum and the pubis force-deflection curves of the iliac or the acetabulum impact were compared with the corridors.

Despite recent advancements in Automated Driving Systems (ADS), the deployment of such systems in dense urban environments still faces a challenging problem: in comparison to motorway or rural driving, urban environments contain a significantly greater number of traffic participants. This makes it difficult to verify the Safety Of The Intended Functionality (SOTIF) across the entire Operational Design Domain (ODD). One approach to solve this problem is to virtually evaluate and verify the safety of the ADS using simulation tools. Whereas traditionally simulated verification has attempted to replicate normal driving conditions, it is possible to achieve superior safety performance by exposing the ADS to more high-risk scenarios than it would otherwise see in the real world. This paper presents the virtual verification process for decision making and motion planning functionalities in urban high-risk edge case scenarios.

Future steering systems must be compatible with automated driving systems to improve cabin space design flexibility. To address this need, a steer-by-wire system with no mechanical linkage is being developed. This paper proposes a control method for a variable steering gear ratio steer-by-wire system with reaction force control to achieve a natural steering feel with a fixed hand position on the steering wheel. Moreover, as a feature of steer-by-wire system, this paper also demonstrates the effectiveness of a control method that transmits only necessary road inputs to the driver.

Tubular sections are found in many automotive structural components such as front rails, cross beams, and sub-frames. They are also used in other vehicular structures, such as buses and rails. In many of these components, smaller tubular sections may be joined together using an adhesive to build the required structure. For crash safety applications, it is important that the joined tube sections be able to provide high energy absorption capability and withstand the impact load before the adhesive bond failure occurs. In this study, single lap tubular joints between two aluminum tubes are investigated for their crush performance at both quasi-static and high impact speeds using finite element analysis. A crash optimized adhesive Betamate 1496 is considered. The joint parameters, such as adhesive overlap length, tube diameters and tube lengths, are varied to determine their effects on energy absorption, peak and mean loads, and tube deformation mode.

This paper proposes a new nonlinear model predictive control (NMPC) for autonomous vehicle path tracking problem. The vehicle is equipped with active rear steering, allowing independent control of front and rear steering. Traditional NMPC, which runs at fixed sampling rate, has been shown to provide satisfactory control performance in this problem. However, the high throughput of NMPC limits its implementation in production vehicle. To address this issue, we propose a novel event-triggered NMPC formulation, where the NMPC is triggered to run only when the actual states deviate from prediction beyond certain threshold. In other words, the event-triggered NMPC will formulate and solve a constrained optimal control problem only if it is enabled by a trigger event. When NMPC is not triggered, the optimal control sequence computed from last NMPC instance is shifted to determine the control action.

The existing indoor test method for tire and vehicle braking performance in the Automated Indoor Braking Analyzer (AIBA) see Ref. 3, is operating fully automatic with standard vehicle braking systems. The tire’s braking performance is evaluated by the braking distance from defined initial speed (e. g. 80 km/h) to standstill of the vehicle, on specified and exchangeable surfaces (e.g. asphalt) under wet or dry road conditions. Vehicle dynamics and control algorithms are intrinsically part of the overall test system. Another known and accredited test procedure for label and type approval is the traction trailer test which currently is used on outdoor proving grounds. With this method the tire will be tested under constant speed while braking until maximum braking force which is the indicator for the tire performance (μ-peak). We introduce in our presentation a new Analytic Test Vehicle which combines advantages of vehicle- and trailer-based test methods.