Adaptive importance sampling for probabilistic validation of advanced driver assistance systems

We present an approach for validation of advanced driver assistance systems, based on randomized algorithms. The new method consists of an iterative randomized simulation using adaptive importance sampling. The randomized algorithm is more efficient than conventional simulation techniques. The importance sampling pdf is estimated by a kernel density estimate, based on the results from the previous iteration. The concept is illustrated with a simple adaptive cruise control problem

Testing advanced driver assistance systems for fault management with the VEHIL test facility

This paper presents a methodological approach for validation of advanced driver assistance systems (ADASs), especially concerning fault management. Tools in this methodology are the unique VEhicle-Hardware-In-the-Loop (VEHIL) test facility and the associated simulation tool PRESCAN. With VEHIL the development process and more specifically the validation phase of intelligent vehicles can be carried out safer, cheaper, more manageable, and more reliable. In VEHIL a complete vehicle is tested in the simulation loop, such that the safety and reliability of an ADAS can be tested to great accuracy and reliability

VEHIL: a test facility for validation of fault management systems for advanced driver assistance systems

We present a methodological approach for the validation of fault management systems for Advanced Driver Assistance Systems (ADAS). For the validation process the unique VEHIL facility, developed by TNO Automotive and currently situated in Helmond, The Netherlands, is applied. The VEHIL facility provides the opportunity to make the entire development process of intelligent vehicles safer, cheaper, and more manageable, and to make simulation more reliable. The main feature of VEHIL is that a complete intelligent vehicle, including its sensors and actuators, can be tested in a Hardware-In-the-Loop simulation environment. In this way VEHIL can be applied in the design phase for fast and easy optimization of the sensor configuration. Moreover, due to its ability for providing very accurately controllable testing conditions, VEHIL can also be used for the validation of the performance of intelligent vehicle control and fault management systems. In this paper, we particularly focus on the use of VEHIL for the validation of fault management systems for Advanced Driver Assistance Systems

VEHIL : test facility for fault management testing of advanced driver assistance systems

This paper presents the latest developments of the VEHIL facility, which aims to make the development process of intelligent vehicles safer, cheaper and more manageable. The main feature of VEHIL is that a complete intelligent vehicle can be tested in a hardware-in-the-loop simulation environment. The use of VEHIL will be illustrated by preliminary test results of a Pre-Crash System. Furthermore, a methodological approach will be presented for the validation of fault management systems for Advanced Driver Assistance Systems by fault injection in VEHIL

Traffic Modelling Validation of Advanced Driver Assistance Systems

This paper presents a microscopic traffic model for the validation of advanced driver assistance systems. This model describes single-lane traffic and is calibrated with data from a field operational test. To illustrate the use of the model, a Monte Carlo simulation of single-lane traffic scenarios is executed with application to cooperative adaptive cruise control system. The model is then validated by comparing the simulation results with data gathered from test drives. ©2007 IEEE

Design and validation of advanced driver assistance systems

This thesis presents new tools and methods for the design and validation of advanced driver assistance systems (ADASs). ADASs aim to improve driving comfort and traffic safety by assisting the driver in recognizing and reacting to potentially dangerous traffic situations. A major challenge in designing these systems is to guarantee high performance and dependability under all possible combinations of traffic scenarios, operating conditions, and failure modes. These stringent requirements necessitate fault-tolerant control techniques and a thorough validation of the system. A microscopic traffic simulation within the simulation environment PreScan supports the initial system design. In addition, a unique tool for the design and validation of ADASs is presented and evaluated: vehicle hardware-in-the-loop (VeHIL) simulation. The VeHIL laboratory allows an ADAS-equipped vehicle to be tested in an artificial environment, where surrounding traffic is emulated by robot vehicles. VeHIL enables repeatable, safe, and accurate testing, complementary to human-in-the-loop test drives. The use of these three tools (PreScan, VeHIL, and test drives) is combined in a methodology for probabilistic validation of ADASs, based on randomized algorithms. This methodology is more efficient than conventional simulation techniques and the current practice of trial-and-error test drives. It results in a test schedule definition with a minimum number of simulations and test runs, such that the performance and dependability of an ADAS can be guaranteed, given a desired level of accuracy and confidence. The added value of the methodology is demonstrated with three case studies, involving a driver information and warning system, a fault-tolerant system for cooperative adaptive cruise control, and a pre-crash system.Trai

PROBABILISTIC VALIDATION OF ADVANCED DRIVER ASSISTANCE SYSTEMS

We present a methodological approach for validation of advanced driver assistance systems, based on randomized algorithms. The new methodology is more efficient than conventional validation by simulations and field tests, especially with increasing system complexity. The methodology consists of first specifying the perturbation set and performance criteria. Then a minimum required number of samples and a relevant sampling space is selected. Next an iterative randomized simulation is executed, followed by validation with hardware tests. The concept is illustrated with a simple adaptive cruise control problem

Experimental evaluation of a communication-based cooperative driving algorithm

This paper presents a cooperative longitudinal control system for a cluster of vehicles using an environment sensor (radar) and vehicle-to-vehicle communication. The controller computes a desired acceleration that is realized by a lower-level control loop to obtain a smooth and safe traffic flow in a string of vehicles. State information from the host vehicle and preceding vehicles, obtained by environment sensing and vehicle-to-vehicle communication, are fused to obtain reliable signals. The system is evaluated on functional performance using test drives