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This paper presents the test track scenario design and analysis used to estimate the performances of heavy vehicles equipped with forward collision warning and automatic emergency braking systems in rear-end crash scenarios. The first part of this design and analysis study was to develop parameters for brake inputs in test track scenarios simulating a driver that has insufficiently applied the brakes to avoid a rear-end collision. In the second part of this study, the deceleration limits imposed by heavy vehicles mechanics and brake systems are used to estimate automatic emergency braking performance benefits with respect to minimum stopping distance requirements set by Federal Motor Vehicle Safety Standards. The results of this study were used to complete the test track procedures and show that all heavy vehicles meeting regulatory stopping distance requirements have the braking capacity to demonstrate rear-end crash avoidance improvements in the developed tests.

The rapid innovation underway with vehicle brake safety systems leads to extensive evaluation and testing by system developers and regulatory agencies. The ability to evaluate complex heavy truck braking systems is potentially more rapid and economical through hardware-in-the-loop (HiL) simulation which employs the actual electronics and vehicle hardware. Though the initial HiL system development is time consuming and expensive, tests conducted on the completed system do not require track time, fuel, vehicle maintenance, or technician labor for driving or truck configuration changes. Truck and trailer configuration and loading as well as test scenarios can be rapidly adjusted within the vehicle dynamics simulation software to evaluate the performance of automated safety interventions (such as ESC) over a wide range of conditions.

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.

This study was performed to showcase the possible applications of the Hardware-in-the-loop (HIL) simulation environment developed by the National Highway Traffic Safety Administration (NHTSA), to test heavy truck crash avoidance safety systems. In this study, the HIL simulation environment was used to recreate a simulation of an actual accident scenario involving a single tractor semi-trailer combination. The scenario was then simulated with and without an antilock brake system (ABS) and electronic stability control (ESC) system to investigate the crash avoidance potential afforded by the tractor equipped with the safety systems. The crash scenario was interpreted as a path-following problem, and three possible driver intended paths were developed from the accident scene data.

Field testing of Automatic Emergency Braking (AEB) systems using real actual heavy trucks and buses is unavoidably limited by the dangers and expenses inherent in crash-imminent scenarios. For this paper, a heavy vehicle is defined as having a gross vehicle weight rating (GVWR) that exceeds 4536 kg (10,000 lbs.). High fidelity Hardware-in-the-Loop (HiL) simulation systems have the potential to enable safe and accurate laboratory testing and evaluation of heavy vehicle AEB systems. This paper describes the setup and experimental validation of such a HiL simulation system. An instrumented Volvo tractor-trailer equipped with a Bendix Wingman Advanced System, including the FLR20 forward looking radar and AEB system, was put through a battery of different types of track tests to benchmark the AEB performance.

Dynamic Brake Support (DBS) is a safety system that has been applied to various passenger cars and has been shown to be effective at assisting drivers in avoiding or mitigating rear-end collisions. The objective of a DBS system is to ensure that the brake system is applied quickly and at sufficient pressure when a driver responds to a collision imminent situation. DBS is capable of improving braking response due to a passenger car driver's tendency to utilize multi-stage braking. Interest is developing in using DBS on commercial vehicles. In order to evaluate the possible improvement in safety that could be realized through the use of DBS, driver braking behavior must first be analyzed to confirm that improvement is possible and necessary. To determine if this is the case, a study of the response of truck drivers' braking behavior in collision imminent situations is conducted. This paper presents the method of evaluation and results.

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.

This paper presents National Highway Traffic Safety Administration’s 2017 and 2018 test track research results with heavy vehicles equipped with forward collision warning and automatic emergency braking systems. Newly developed objective test procedures were used to perform and collect performance data with three single-unit trucks equipped with the crash avoidance systems. The results of this research show that the test procedures are applicable to many heavy vehicles and indicate that performance improvements in heavy vehicles equipped with these safety systems can be objectively measured.

This paper describes the National Highway Traffic Safety Administration's (NHTSA) efforts to determine how different outrigger designs can affect J-Turn and Road Edge Recovery test maneuver outcome. Data were collected during tests performed with three different outrigger designs (made from aluminum, carbon fiber, and titanium) having different physical properties (geometry and weight). Four sport utility vehicles were tested: a 2001 Chevrolet Blazer, 2001 Toyota 4Runner, 2001 Ford Escape, and a 1999 Mercedes ML320. The 4Runner and ML320 were each equipped with electronic stability control, however the systems were disabled for the tests performed in this study. A detailed description of the testing performed and the results obtained are discussed. From the results, a comparison of how the three outrigger designs affected the test results is provided.

This research focused on developing a methodology for a vehicle dynamics model of a passenger vehicle outfitted with an aftermarket Automated Driving System software package using only literature and track based results. This package consisted of Autoware.AI (Autoware ®) operating on Robot Operating System 1 (ROS™) with C++ and Python ®. Initial focus was understanding the basics of ROS and how to implement test scenarios in Python to characterize the control systems and dynamics of the vehicle. As understanding of the system continued to develop, test scenarios were adapted to better fit system characterization goals with identification of system configuration limits. Trends from on-track testing were identified and paired with first-order linear systems to simulate physical vehicle responses to given command inputs. Sub-models were developed and simulated in MATLAB ® with command inputs from on-track testing.