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Connectivity in ground vehicles allows vehicles to share crucial vehicle data, such as vehicle acceleration and speed, with each other. Using sensors such as radars and lidars, on the other hand, the intravehicular distance between a leader vehicle and a host vehicle can be detected. Cooperative Adaptive Cruise Control (CACC) builds upon ground vehicle connectivity and sensor information to form convoys with automated car following. CACC can also be used to improve fuel economy and mobility performance of vehicles in the said convoy. In this paper, a CACC system is presented, where the acceleration of the lead vehicle is used in the calculation of desired vehicle speed. In addition to the smooth car following abilities, the proposed CACC also has the capability to calculate a speed profile for the ego vehicle that is fuel efficient, making it an Ecological CACC (Eco-CACC) model.

This paper compares the results from three human factors studies conducted in a motion-based simulator in 2008, 2014 and 2023, to highlight the trends in driver's response to Forward Collision Warning (FCW). The studies were motivated by the goal to develop an effective HMI (Human-Machine Interface) strategy that enables the required driver's response to FCW while minimizing the level of annoyance of the feature. All three studies evaluated driver response to a baseline-FCW and no-FCW conditions. Additionally, the 2023 study included two modified FCW chime variants: a softer FCW chime and a fading FCW chime. Sixteen (16) participants, balanced for gender and age, were tested for each group in all iterations of the studies. The participants drove in a high-fidelity simulator with a visual distraction task (number reading). After driving 15 minutes in a nighttime rural highway environment, a surprise forward collision threat arose during the distraction task.

Improving passenger safety inside vehicle cabins requires continuously monitoring vehicle seat occupancy statuses. Monitoring a vehicle seat’s occupancy status includes detecting if the seat is occupied and classifying the seat’s occupancy type. This paper introduces an innovative non-intrusive technique that employs capacitive sensing and an occupancy classifier to monitor a vehicle seat’s occupancy status. Capacitive sensing is facilitated by a meticulously constructed capacitance-sensing mat that easily integrates with any vehicle seat. When a passenger or an inanimate object occupies a vehicle seat equipped with the mat, they will induce variations in the mat’s internal capacitances. The variations are, in turn, represented pictorially as grayscale capacitance-sensing images (CSI), which yield the feature vectors the classifier requires to classify the seat’s occupancy type.

Occupant protection in side impacts, in particular for near-side occupants, is a challenge due to the occupant’s close proximity to the impact. Near-side occupants have limited space to ride down the impact. Curtain and side airbags fill the gap between occupant and the side interior. This analysis was conducted to provide insight on the characteristics of side impacts and the relevancy of currently regulated test configurations. For this purpose, 2007-2015 NASS-CDS and 2017-2021 CISS side crash data were analyzed for towed light vehicles. 2008 and newer model year vehicle data was selected to ensure that most vehicles were equipped with side/curtain airbags. The results showed that side impacts accounted for approximately 26.7% of the vehicles involved and 18.9% of the vehicles with at least one seriously injured occupant. Most side impacts involved damage to the front and front-to-center of the vehicle.

Corrosion affects all industrial sectors where metals or metal alloys are used in their structures. In the automotive industry, the continuous search for lightweight parts has increased the demand for effective corrosion protection, in order to improve vehicle performance without compromising durability and safety. In this scenario, coatings are essential elements to preserve and protect vehicle parts from various environmental aggressions. Automotive coatings can be classified into primers, topcoats, clearcoats, and specialty coatings. Primers provide corrosion resistance and promote adhesion between the substrate and topcoat. Topcoats provide color, gloss, and durability to the coating system, while clearcoats enhance the appearance and durability of the finish. Specialty coatings provide additional properties, such as scratch resistance, chemical resistance, and UV protection.

In order to evaluate the THOR-50M as a front impact Anthropomorphic Test Device (ATD) for vehicle safety design, the ATD was compared to the H3-50M in matching vehicle crash tests for 20 unique vehicle models from 2 vehicle manufacturers. For the belted driver condition, a total of fifty-four crash tests were investigated in the 56.3 km/h (35 mph) front rigid barrier impact condition. Four more tests were compared for the unbelted driver and right front passenger at 40.2 km/h (25 mph) in the flat frontal and 30-degree right oblique rigid barrier impact conditions. The two ATDs were also evaluated for their ability to predict injury risk by comparing their fleet average injury risk to Crash Investigation Sampling System (CISS) accident data for similar conditions. The differences in seating position and their effect on ATD responses were also investigated.

This is an extension of simple fuel consumption modeling toward HEV. Previous work showed that in urban driving the overhead of running an ICEV engine can use as much fuel as the traction work. The bidirectional character and high efficiency of electric motors enables HEVs to run as a BEV at negative and low traction powers, with no net input from the small battery. The ICE provides the net work at higher traction powers where it is most efficient. Whereas the network reduction is the total negative work times the system round-trip efficiency, the reduction in engine running time requires knowledge of the distribution of traction power levels. The traction power histogram, and the work histogram derived from it, provide the required drive cycle description. The traction power is normalized by vehicle mass, so that the drive trace component becomes invariant, and the road load component nearly invariant to vehicle mass.

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 by communicating accurate, real-time information. In this paper, we propose a novel Consolidated Object Data Service (CODS) for multi-Radio Access Technology (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.

To enable smooth and low-risk autonomous driving in the presence of other road users, such as cyclists and pedestrians, appropriate predictive safe speed control strategies relying on accurate and robust prediction models should be employed. However, difficulties related to driving scene understanding and a wide variety of features influencing decisions of other road users significantly complexifies prediction tasks and related controls. This paper proposes a hierarchical neural network (NN)-based prediction model of pedestrian crossing behavior, which is aimed to be applied within an autonomous vehicle (AV) safe speed control strategy. Additionally, different single-level prediction models are presented and analyzed as well, to serve as baseline approaches.

Driver oblique far-side sled impacts were simulated with three surrogates. The EuroSID side impact dummy with rib extension (ES2re), the WorldSID side impact 50th percentile male dummy (WS50M), and the Global Human Body Modeling Consortium’s 50th percentile male human body (GHBM) models. The versions of the surrogates’ models were 7.0, 7.5.1, and 5.0, respectively. Surrogates were seated in the front left driver seat in a virtual generic crossover sled environment. The Finite Element (FE) based environment consisted of a driver seat, a center console, and a passenger seat. Two restraint systems were considered for each surrogate: belt only (BO) and belt plus a generic seat-mounted far-side impact airbag (BB). Surrogates were restrained using a 3-point belt that has a digressive shoulder force load limiter, and retractor, and anchor pretensioners. The far-side airbag used was a 37-liter in volume and has two chambers.

Lithium-ion batteries (LIBs) have been used as the main power source for Electric vehicles (EVs) in recent years. The mechanical behavior of LIBs subject to crush loading is crucial in assessing and improving the impact safety of battery systems and EVs. In this work, a detailed 3D finite element model for a commercial vehicle battery was built, in order to better understand battery failure behavior under various loading conditions. The model included the major components of a prismatic battery jellyroll, i.e., cathodes, anodes, and separators. The models for these components were validated against the corresponding material coupon tests (e.g., tension and compression). Then the components were integrated into the cell level model for simulation of jellyroll loading and damage behavior under three types of compressive indenter loading: (1) Flat-end punch, (2) Hemispherical punch and (3) Round-edge wedge. The comparisons showed reasonable agreement between modeling and experiments.

Currently the automotive industry has been under extremely important technological changes. Part of these changes are related to the way that users interact with the vehicle and fundamental components are the new digital cluster and screens. These devices have created a disruption in the way information is transmitted to the user, being essential for vehicle operation, including safety. Due to new operating conditions, multiple evaluations need to be performed, one of them is the solar temperature Load to ensure correct operation without compromising user safety. This test is required to identify the thermal performance on the screens mounted on the instrument panel. The performance identification is performed on both sides, analytical and physical. In regards finite element simulation it represents the solar chamber as the main source of heat and being the main mechanism of transmission the radiation.

For Automated Vehicles (AVs) to be successful, they must integrate into society in a way that makes everyone confident in how AVs work to serve people and their communities. This integration requires that AVs communicate effectively, not only with other vehicles, but with all road users, including pedestrians and cyclists. One proposed method of AV communication is through an external human-machine interface (eHMI). While many studies have evaluated eHMI solutions, few have considered their compliance with relevant Federal Motor Vehicle Safety Standards (FMVSS) and their scalability. This study evaluated the effectiveness of a lightbar eHMI to communicate AV intent by measuring user comprehension of the eHMI and its impact on pedestrians’ trust and acceptance of AVs.

This study focused on occupant responses in very large pickup trucks in rollovers and was conducted in three phases. Phase 1 - Field data analysis: In a prior study [9], 1998 to 2020 FARS data were analyzed; Pickup truck drivers with fatality were 7.4 kg heavier and 4.6 cm taller than passenger car drivers. Most pickup truck drivers were males. Phase 1 extended the study by focusing on the drivers of very large pickup trucks. The size of 1999-2016 Ford F-250 and F-350 drivers involved in fatal crashes was analyzed by age and sex. More than 90% of drivers were males. The average male driver was 179.5 ± 7.5 cm tall and weighed 89.6 ± 18.4 kg. Phase 2 – Surrogate study: Twenty-nine male surrogates were selected to represent the average size of male drivers of F-250 and F-350s involved in fatal crashes. On average, the volunteers weighed 88.6 ± 5.2 kg and were 180.0 ± 3.2 cm tall with a 95.2 ± 2.2 cm seated height.

Electric vehicles (EVs) are a promising and proven technology for achieving sustainable mobility with zero carbon emissions, very low noise pollution, and reducing the dependency on fossil fuels. Global EV sales have been increasing by ~110 % since 2015, with a significant rise in 2021 (~6.75 mils EV registered) mainly led by China, the US, and Europe, amplifying the EV market share to 8.3% compared to 4.2% in 2020. Future developments aimed at designing better batteries and charging technologies that reduce charging time, reduce initial battery cost, and increased flexibility. In India, EVs are emerging significantly due to stringent Carbon di Oxide (CO2) reduction drives, increasing crude oil prices, and the availability of cheaper renewable energy. Leveraging government promotional policies, evolving the entire ecosystem, globally advantageous manufacturing costs, and competitive engineering skills form the perfect blend for India.

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.

The previously developed power-based fuel consumption theory for Internal Combustion Engine Vehicles (ICEV) is extended to Battery Electric Vehicles (BEV). The main difference between the BEV model structure and the ICEV is the bi-directional character of traction motors and batteries. A traction motor model was developed as a bi-linear function of positive and negative traction power. Another difference is that the accessories and cabin heating are powered directly from the battery, and not from the powertrain. The resulting unified model for ICEV and BEV energy consumption has linear terms proportional to positive and negative traction power, accessory power, and overhead, in varying proportions. Compared to the ICEV, the BEV powertrain has a high marginal efficiency and low overhead. As a result, BEV energy consumption data under a wide range of driving conditions are mainly proportional to net traction power, with only a small offset.

As electric vehicles are becoming increasingly popular and necessary for the future mobility needs of civilization, further effort is continually made to improve the efficiency, cost, and safety of the lithium-ion battery packs that power these vehicles. To facilitate these goals, this paper introduces a data driven model to predict a distribution of surface temperatures for a lithium-ion battery pack: a long short-term memory (LSTM) neural network. The LSTM model is trained and validated with lithium-ion cells electrically connected to form a battery pack. Voltage, current, state of charge (SOC), and cell surface temperature from two arrays are used as inputs from a wide range of high and low temperature drive cycles. Additionally, ambient temperature is added as an input to the LSTM model.

This study was conducted to assess the effects of differing rear impact pulse characteristics on restraint performance, front-seat occupant kinematics, biomechanical responses, and seat yielding. Five rear sled tests were conducted at 40.2 km/h using a modern seat. The sled buck was representative of a generic sport utility vehicle. A 50th percentile Hybrid III ATD was used. The peak accelerations, acceleration profiles and durations were varied. Three of the pulses were selected based on published information and two were modeled to assess the effects of peak acceleration occurring early and later within the pulse duration using a front and rear biased trapezoidal characteristic shape. The seatback angle at maximum rearward deformation varied from 46 to 67 degrees. It was lowest in Pulse 1 which simulates an 80 km/h car-to-car rear impact.

Assessment of the physical evidence on a seat belt restraint system provides one source of data for determining an occupant’s seat belt use or non-use during a motor vehicle crash. The evidence typically associated with loading from a restrained occupant has been extensively researched and documented in the literature. However, evidence of loading to the restraint system can also be generated by other means, including the interaction of an unrestrained occupant with a stowed restraint system. The present study evaluates physical evidence on multiple stowed restraint systems generated via interaction with unrestrained occupants during a full-scale dolly rollover crash test of a large multiple passenger van. Unbelted anthropomorphic test devices (ATDs) were positioned in the driver and right front passenger seats and in all designated seating positions in the third, fourth, and fifth rows.