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Automated driving systems (ADS) are designed toward safely navigating the roadway environment, which also includes consideration of potential conflict with other road users. Of particular concern is understanding the cumulative risk associated with vulnerable road users (VRUs) conflicts and collisions. VRUs represent a population of road users that have limited protection compared to vehicle occupants. These severity distributions are particularly useful in evaluating ADS real-world performance with respect to the existing fleet of vehicles. The objective of this study was to present event severity distributions associated with vehicle-cyclist collisions within an urban naturalistic driving environment by leveraging data from third-party vehicles instrumented with forward-facing cameras and a sensor suite (accelerometer sampling at 20 Hz and GPS [variable sampling frequency]). From over 66 million miles of driving, 30 collision events were identified.

In recent years, the discussion around the advent of highly automated vehicles has shifted from “if” to “when.” Commercially available vehicles already incorporate automated vehicle (AV) technologies of varying capability, and the eventual transition to fully automated systems, at least within certain predefined Operational Design Domains, is largely considered inevitable. While the full ramifications of this shift and the eventual depreciation of human driver control are still under intense debate, there is broad agreement on one issue -the advent of driverless systems will remove several constraints on the design of vehicle interior spaces, creating the opportunity for innovation. Even at this early stage, ambitious design concepts of purpose specific vehicles - mobile gyms, offices, bedrooms - have been proposed. More grounded designs, such as rotating passenger seats, have also been put forward.

Rear-end impacts are the most common crash scenario in the United States. Although automated vehicle (AV) technologies, such as frontal crash warning (FCW) and automatic emergency braking (AEB), are mitigating and preventing rear-end impacts, the technology is only gradually being introduced and currently has only limited effectiveness. Accordingly, there is a need to evaluate the current state of passive safety technologies, including the performance of seatbacks and head restraints. The objective of this study was to examine trends in head and neck loading during rear impact testing in new vehicle models over the prior decade. Data from 601 simulated rear impact sled tests (model years 2004 to 2018) conducted as a part of the Insurance Institute for Highway Safety (IIHS) Vehicle Seat/Head Restraint Evaluation Protocol were obtained.

There are over 4,500 fatal intersection crashes each year in the United States. Intersection Advanced Driver Assistance Systems (I-ADAS) are emerging active safety systems designed to detect an imminent intersection crash and either provide a warning or perform an automated evasive maneuver. The performance of an I-ADAS will depend on the ability of the onboard sensors to detect an imminent collision early enough for an I-ADAS to respond in a timely manner. One promising method for determining the earliest detection opportunity is through the reconstruction of real-world intersection crashes. After determining the earliest detection opportunity, the required sensor range, orientation, and field of view can then be determined through the simulation of these crashes as if the vehicles had been equipped with an I-ADAS.