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Testing vision-based advanced driver assistance systems (ADAS) in a Camera-in-the-Loop (CiL) bench setup, where external visual inputs are used to stimulate the system, provides an opportunity to experiment with a wide variety of test scenarios, different types of vehicle actors, vulnerable road users, and weather conditions that may be difficult to replicate in the real world. In addition, once the CiL bench is setup and operating, experiments can be performed in less time when compared to track testing alternatives. In order to better quantify normal operating zones, track testing results were used to identify behavior corridors via a statistical methodology. After determining normal operational variability via track testing of baseline stationary surrogate vehicle and pedestrian scenarios, these operating zones were applied to screen-based testing in a CiL test setup to determine particularly challenging scenarios which might benefit from replication in a track testing environment.

In order to validate the operation of advanced driver assistance systems (ADAS), tests must be performed that assess the performance of the system in response to different scenarios. Some of these systems are designed for crash-imminent situations, and safely testing them requires large stretches of controlled pavement, expensive surrogate targets, and a fully functional vehicle. As a possible more-manageable alternative to testing the full vehicle in these situations, this study sought to explore whether these systems could be isolated, and tests could be performed on a bench via a hardware-in-the-loop methodology. For camera systems, these benches are called Camera-in-the-Loop (CiL) systems and involve presenting visual stimuli to the device via an external input.

Advanced Driver Assistance System (ADAS) has become a common standard feature assisting greater safety and fuel efficiency in the latest automobiles. Yet some ADAS systems fail to improve driving comfort for vehicle occupants who expect human-like driving. One of the more difficult situations in ADAS-assisted driving involves instances with cut-in vehicles. In vehicle control, determining the moment at which the system recognizes a cut-in vehicle as an active target is a challenging task. A well-designed comprehensive threat assessment developed for cut-in vehicle driving scenarios should eliminate abrupt and excessive deceleration of the vehicle and produce a smooth and safe driving experience. This paper proposes a novel methodology for threat assessment for driving instances involving a cut-in vehicle. The methodology takes into consideration kinematics, vehicle dynamics, vehicle stability, road condition, and driving comfort.