Search Results

Heavy Vehicle Event Data Recorders (HVEDRs) have the ability to capture important data surrounding an event such as a crash or near crash. Efforts by many researchers to analyze the capabilities and performance of these complex systems can be problematic, in part, due to the challenges of obtaining a heavy truck, the necessary space to safely test systems, the inherent unpredictability in testing, and the costs associated with this research. In this paper, a method for simulating vehicle speed sensor (VSS) inputs to HVEDRs to trigger events is introduced and validated. Full-scale instrumented testing is conducted to capture raw VSS signals during steady state and braking conditions. The recorded steady state VSS signals are injected into the HVEDR along with synthesized signals to evaluate the response of the HVEDR. Brake testing VSS signals are similarly captured and injected into the HVEDR to trigger an event record.

Since the early 1990’s, commercial vehicles have suffered from repeated vulnerability exploitations that resulted in a need for improved automotive cybersecurity. This paper outlines the strategies and challenges of implementing an automotive Zero Trust Architecture (ZTA) to secure intra-vehicle networks. Zero Trust (ZT) originated as an Information Technology (IT) principle of “never trust, always verify”; it is the concept that a network must never assume assets can be trusted regardless of their ownership or network location. This research focused on drastically improving security of the cyber-physical vehicle network, with minimal performance impact measured as timing, bandwidth, and processing power. The automotive ZTA was tested using a software-in-the-loop vehicle simulation paired with resource constrained hardware that closely emulated a production vehicle network.

SAE J115 specifies the relevant ISO standards for application to safety labels for use on off-road work machines as defined in SAE J1116.

Rapid Urbanisation, in recent times, has created an exponential demand for light commercial vehicles. Electric vehicles are seen as a way to reduce the impact of emissions due to transportation in urban areas. Due to the growth of e-commerce, commercial transportation, and particularly last-mile delivery, is anticipated to increase. In this context, electric light commercial vehicles (eLCVs) have the potential to be a promising solution by tackling the emission impacts, ensuring faster delivery along with ideal running costs and payload capacity. To increase the range of electric vehicles, it has to be designed for lighter weight with optimal performance in order to meet the user requirements. Cargo capacity and payload have to be taken into account while design & validating the vehicle structure under static and dynamic conditions. Simulation driven product development will help the design team to account for the possible design failure cases at system and vehicle level.

Volvo Trucks enters electrification's next phase With electric trucks already available, the OEM focuses on refining service and maintenance, expanding EV-certified dealerships and scaling production. Why agriculture will automate before on-highway Danfoss' top autonomy executive says automation will help overcome labor and technological challenges that would otherwise leave billions of dollars' worth of crops rotting in fields. Driver-in-the-loop for off-highway development Industrial and agricultural vehicle engineers can draw many of the same benefits from a driving simulator as passenger-car development teams.

Dry dust testing of vehicles on unpaved dust roads plays a crucial role in the development process of automotive manufacturers. One of the central aspects of the test procedure is ensuring the functionality of locking systems in the case of dust ingress and keeping the dust below a certain concentration level inside the vehicle. Another aspect is the customer comfort because of dust deposited on the surface of the car body. This also poses a safety risk to customers when the dust settles on safety-critical parts such as windshields and obstructs the driver’s view. Dust deposition on sensors is also safety critical and is becoming more important because of the increasing amount of sensors for autonomous driving. Nowadays, dust tests are conducted experimentally at dust proving grounds. To gain early insights and avoid costly physical testing, numerical simulations are considered a promising approach. Simulations of vehicle contamination by dry dust have been studied in the past.

The purpose of this study was to construct driver models using long short-term memory (LSTM) in car-following situations, where other vehicles change lanes and cut in front of the ego vehicle (EGV). The development of autonomous vehicle systems (AVSs) using personalized driver models based on the individual driving characteristics of drivers is expected to reduce their discomfort with vehicle control systems. The driving characteristics of human drivers must be considered in such AVSs. In this study, we experimentally measured data from the EGV and other vehicles using a driving simulator consisting of a six-axis motion device and turntable. The experimental scenario simulated a traffic congestion scenario on a straight section of a highway, where a cut-in vehicle (CIV) changed lanes from an adjacent lane and entered in between the EGV and preceding vehicle (PRV).

There have been many studies regarding the stability of vehicles following a sudden air loss event in a tire. Previous works have included literature reviews, full-scale vehicle testing, and computer modeling analyses. Some works have validated physics-based computer vehicle simulation models for passenger vehicles and other works have validated models for heavy commercial vehicles. This work describes a study wherein a validated vehicle dynamics computer model has been applied to extrapolate results to higher event speeds that are consistent with travel speeds on contemporary North American highways. This work applies previously validated vehicle dynamics models to study the stability of a five-axle commercial tractor-semitrailer vehicle following a sudden air loss event for a steer axle tire. Further, the work endeavors to understand the analytical tire model for tires that experience a sudden air loss.

This SAE Standard defines methods and apparatus to evaluate electronic devices for immunity to potential interference from conducted transients along battery feed or switched ignition inputs. Test apparatus specifications outlined in this procedure were developed for components installed in vehicles with 12-V systems (passenger cars and light trucks, 12-V heavy-duty trucks, and vehicles with 24-V systems). Presently, it is not intended for use on other input/output (I/O) lines of the device under test (DUT).

This SAE Standard defines methods and apparatus to evaluate electronic devices for immunity to potential interference from conducted transients along battery feed or switched ignition inputs. Test apparatus specifications outlined in this procedure were developed for components installed in vehicles with 12-V systems (passenger cars and light trucks, 12-V heavy-duty trucks, and vehicles with 24-V systems). Presently, it is not intended for use on other input/output (I/O) lines of the device under test (DUT).

In order to overcome the problems such as ignoring the lack of depth information in the process of perspective projection, or sensitive to surveillance video quality that the existing vehicle motion state solution methods based on video image, this paper presents a methodology for reconstructing traffic accident based on surveillance video and scene point cloud. Firstly, the 2D-3D corresponding points from surveillance video image and scene point cloud are used to estimate the camera spatial pose, and then the Camshift algorithm is used to track the vehicle features and obtain the sequence of vehicle feature pixels. Secondly, the vehicle feature spatial position analysis model is constructed to analysis vehicle feature spatial position sequence, next the vehicle trajectory information is obtained by polynomial function fitting, and the vehicle speed information is obtained by feature spatial position Euclidean distance.

The driving conditions of commercial logistics vehicles have the characteristics of combined urban and suburban roads with relatively fixed mileage and cargo load alteration, which affect the vehicular fuel economy. To this end, an adaptive equivalent consumption minimization strategy (A-ECMS) with vehicle speed and weight recognition is proposed to improve the fuel economy for a range-extender electric van for logistics in this work. The driving conditions are divided into nine representative groups with different vehicle speed and weight statuses, and the driving patterns are recognized with the use of the bagged trees algorithm through vehicle simulations. In order to generate the reference SOC near the optimal values, the optimal SOC trajectories under the typical driving cycles with different loads are solved by the shooting method and the optimal slopes for these nine patterns are obtained.

Thermal management of an electric vehicle plays a vital role for optimum performance and utilization of the available energy. Coolant circuits forms an important module in the overall vehicle architecture to achieve this. It is highly necessary to ensure that required quantity of the coolant is filled in the coolant loop. However, coolant deficit due to trapped air in the circuit might pose operational risks and system failure due to inadequate cooling. In this regard, CFD simulation approaches are proven to have an edge in visualizing the flow field, thereby, identifying the root cause of problem in coolant filling process. Using commercial CFD software Star-CCM+ as a tool, current work addressed the issue of removing trapped air inside the circuits by systematic filling approach. Studies have been carried out to understand the coolant filling fractions achieved with traditional filling method on battery loop.

The concept of autonomous driving is becoming increasingly familiar in the automotive and “in-door” automation systems fields. Furthermore, the industrial development is focusing its efforts on industry 4.0, whose some main features are data transfer, programming, systems interconnection and automation. The agricultural sector just recently has experienced the first examples of autonomous agricultural vehicles, although agricultural mechanization has reached a good level of automation. Indeed, many examples of automatic machineries are already present in the market such as little robots for the execution of some operations. This work focuses on modelling and simulation of a self-driving orchard tractor. The main goal was to reproduce the behaviour of the specialized vehicle, moving in an orchard or a vineyard and conducting automatic or semi-automatic operations.

Many automated driving technologies have been developed and are continuing to be implemented for practical use. Among them a driver model is used in automated driving and driver assistance systems to control the longitudinal and lateral directions of the vehicles that reflect the characteristics of individual drivers. To this end, personalized driver models are constructed in this study using long short-term memory (LSTM). The driver models include individual driving characteristics and adapt system control to help minimize discomfort and nuisance to drivers. LSTM is used to construct the driver model, which includes time-series data processing. LSTM models have been used to investigate pedestrian behaviors and develop driver behavior models in previous studies. We measure the driving operation data of the driver using a driving simulator (DS).

Heavy-duty commercial vehicles consume a significant amount of energy due to their large size and mass, directly leading to vehicle operators prioritizing energy efficiency to reduce operational costs and comply with environmental regulations. One tool that can be used for the evaluation of energy efficiency in heavy-duty vehicles is the evaluation of energy efficiency using vehicle modeling and simulation. Simulation provides a path for energy efficiency improvement by allowing rapid experimentation of different vehicle characteristics on fuel consumption without the need for costly physical prototyping. The research presented in this paper focuses on using real-world, sparsely sampled telematics data from a large fleet of heavy-duty vehicles to create high-fidelity models for simulation. Samples in the telematics dataset are collected sporadically, resulting in sparse data with an infrequent and irregular sampling rate.

Recently, application of a Multi-axis road simulator has been studied to improve reproducibility of vehicle running, however the system is complicated and expensive due to the large number of vibration axis. Therefore, it is necessary to clarify the evaluation object and balance the evaluation accuracy with the test efficiency appropriately. In this study, we have developed a method for efficient fatigue reliability evaluation of an off-road motorcycle with the road simulator. We focused on the simulation of a jump section of a track because it had been clarified that the fatigue damage in the jump section accounts for the most of the fatigue damage to the vehicle in one lap on the track.

In this research project finalized in 2019 the potential of electrification of multi-purpose tractors/machines have been studied, and what impact it would have on the holistic city eco system. The study shows there is great technical potential for electrification and connectivity, while it also seems to be a high acceptance among users and stakeholders (e.g. positive to having a partly electrified fleet). Today a lot of attention all over the world is directed to more sustainable cities and thereby electrification of vehicles. Unlike electrification of bigger machines (e.g. rear loaders), the challenge with electrification of smaller multi-purpose tractors is their large range of usage in combination with the variation of tools and tool suppliers. Methods used were benchmark/research, user research, and simulations of driving cycles based on user scenarios. Simulation models for power requirements were developed in MATLAB/Simulink.

Abstract To solve the problem that it is difficult for drivers to control the vehicle at low speed, a new setting scheme of pedal map is proposed to ensure that the vehicle has the speed controllability in the full speed range. In this scheme, based on obtaining the maximum and minimum driving characteristics of the vehicle and the driving resistance characteristics of the vehicle, the pedal map is divided into a sensitive area and insensitive area. In the insensitive area, acceleration hysteresis is formed, which ensures that the throttle is slightly fluctuated and has good speed stability. At the same time, the sensitive area of the accelerator pedal is formed far away from the driving resistance curve to ensure that the vehicle has a great acceleration ability. To verify the effectiveness of the proposed scheme, the data of a commercial vehicle is selected for the design of the pedal map, and the driver-vehicle closed-loop test based on the driving simulator is conducted.

Abstract The present work deals with the damage life correlation of vehicle-level testing results of an axle housing for different road load conditions with the accelerated bench testing experiment results to reduce product development time. Also failure analysis is carried out to overcome the mechanical strength losses caused by the hot forming process during the manufacturing of housings. Commercial vehicle torture test tracks are built to reflect the forces similar to vehicle usage conditions from lighter to severe loadings. Strain data and calibrated force values are captured at the critical loading points in the axle for one cycle, at actual vehicle-driven speeds, to reflect the accelerated load values on five different track conditions. Damages estimation carried out based on the road loads reflects there will be no failure of axle housings till the acceptance of 120 repeats in different track combinations.