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The Monte Carlo method is a well-known technique for propagating uncertainty in complex systems and has been applied to traffic crash reconstruction analysis. The Monte Carlo method is a probabilistic technique that randomly samples input distributions and then combines these samples according to a deterministic model. However, describing every input variable as a distribution requires knowledge of the distribution, which may or may not be available, and the time and expense of determining the distribution parameters may be prohibitive. Therefore, the most influential parameters from the input data, such as mean values, standard deviations, shape parameters, and correlation coefficients, can be determined using an analytical sensitivity calculation based on the score function.

Recovery of snapshot data recorded by Caterpillar engine control modules (ECMs) using Caterpillar Electronic Technician (CatET) software requires a complete snapshot record that contains information gathered both before and after an event. However, if an event is set and a crash ensues, or a crash creates an event, then it is possible for the ECM to lose power and not complete the recording. As such, the data may not be recoverable with CatET maintenance software. An examination of the J1708 network traffic reveals the snapshot data does exist and is recoverable. A motivational case study of a crash test between a Caterpillar powered school bus and a parked transit bus is presented to establish the hypothesis. Subsequently, a digital forensic recovery algorithm is detailed as it is implemented in the Synercon Technologies Forensic Link Adapter (FLA).

Maintaining and diagnosing vehicle systems often involves a technician connecting a service computer to the vehicle diagnostic port through a vehicle diagnostics adapter (VDA). This creates a connection from the service software to the vehicle network through a protocol adapter. Often, the protocols for the personal computer (PC) hosted diagnostic programs use USB, and the diagnostic port provides access to the controller area network (CAN). However, the PC can also communicate to the VDA via WiFi or Bluetooth. There may be scenarios where these wireless interfaces are not appropriate, such as maintaining military vehicles. As such, a method to defeature the wireless capabilities of a typical vehicle diagnostic adapter is demonstrated without access to the source code or modifying the hardware. The process of understanding the vehicle diagnostic adapter system, its hardware components, the firmware for the main processor and subsystems, and the update mechanism is explored.

Autonomous truck and trailer configurations face challenges when operating in reverse due to the lack of sensing on the trailer. It is anticipated that sensor packages will be installed on existing trailers to extend autonomous operations while operating in reverse in uncontrolled environments, like a customer's loading dock. Power Line Communication (PLC) between the trailer and the tractor cannot support high bandwidth and low latency communication. This paper explores the impact of using Ethernet or a wireless medium for commercial trailer-tractor communication on the lifecycle and operation of trailer electronic control units (ECUs) from a Systems Engineering perspective to address system requirements, integration, and security. Additionally, content-based and host-based networking approaches for in-vehicle communication, such as Named Data Networking (NDN) and IP-based networking are compared.

The proper investigation of crashes involving commercial vehicles is critical for fairly assessing liability and damages, if they exist. In addition to traditional physics based approaches, the digital records stored within heavy vehicle electronic control modules (ECMs) are useful in determining the events leading to a crash. Traditional methods of extracting digital data use proprietary diagnostic and maintenance software and require a functioning ECM. However, some crashes induce damage that renders the ECM inoperable, even though it may still contain data. As such, the objective of this research is to examine the digital record in an ECM and understand its meaning. The research was performed on a Detroit Diesel DDEC V engine control module. The data extracted from the flash memory chips include: Last Stop Record, two Hard Brake events, and the Daily Engine Usage Log. The procedure of extracting and reading the memory chips is explained.

Vehicles using controller area networks (CANs) for on-board device communications may have event data recorders (EDRs) that either capture or reflect network feeds from an array of sensors and other electronic control units. Using the data recorded in an EDR for investigative purposes requires external verification of accuracy. However, conducting external tests that set events can be expensive due to the time and equipment involved. This paper proposes a practical verification method that uses CAN bus monitoring tools to compare vehicle network traffic to external measurements. The premise of this work is that data reliability from an EDR can be determined if the reliability of the network data source for the EDR can be determined. Once the reliability of the source is determined, the reliability of the event data can be quantified based on effects of truncation and sampling.

Concepts of forensic soundness as they are currently understood in the field of digital forensics are related to the digital data on heavy vehicle electronic control modules (ECMs). An assessment for forensic soundness addresses: 1) the integrity of the data, 2) the meaning of the data, 3) the processes for detecting or predicting errors, 4) transparency of the operation, and 5) the expertise of the practitioners. The integrity of the data can be verified using cryptographic hash functions. Interpreting and understanding the meaning of the data is based on standards or manufacturer software. Comparison of interpreted ECM data to external reference measurements is reviewed from the current literature. Meaning is also extracted from interpreting hexadecimal data based on the J1939 and J1587 standards. Error detection and mitigation strategies are discussed in the form of sensor simulators to eliminate artificial fault codes.