Constraints and Solutions in Creating an Efficient Validation Framework for RADAR based Electronic Control Units
Abstract Advanced driver assistance features like Advanced Emergency Brake Assist, Adaptive Cruise Control, Blind Spot Monitoring, Stop and Go, Pedestrian Detection, Obstacle Detection and Collision Detection are becoming mandatory in many countries. This is because of the promising results received in reducing 75% of fatalities related to road accidents. All these features use RADAR in detecting the range, speed and even direction of multiple targets using complex signal processing algorithm. Testing such ECUs is becoming too difficult considering the fact that the RADAR is integrated in the PCB of ECU. Hence the simulation of RADAR sensor for emulation of various real world scenarios is not a preferred solution for OEMs. Furthermore, Tier ones are not interested in a testing solution where the real RADAR sensor is bypassed. This paper discusses such issues which include the validation of the most modern Electronic Scanning RADARs.
Multi-Target Tracking Algorithm in the Complicated Road Condition for Automotive Millimeter-wave Radar
Abstract Automotive radar is the most important component in the autonomous driving system, which detects the obstacles, vehicles and pedestrians around with acceptable cost. The target tracking is one of the key functions in the automotive radar which estimates the position and speed of the targets having regarding to the measurement inaccuracy and interferences. Modern automotive radar requires a multi-target tracking algorithm, as in the radar field of view hundreds of targets can present. In practice, the automotive radar faces very complicated and fast-changing road conditions, for example tunnels and curved roads. The targets’ unpredictable movements and the reflections of the electromagnetic wave from the tunnel walls and the roads will make the multi-target tracking a difficult task. Such situation may last several seconds so that the continuous tracks of the targets cannot be maintained and the tracks are dropped mistakenly.
RACam  is an Active Safety product designed and manufactured at Delphi and is part of their ADAS portfolio. It combines two sensors - Electronically Scanned RADAR and Camera in a single package. RADAR and Vision fusion data is used to realize safety critical systems such as Adaptive Cruise Control (ACC), Autonomous Emergency Braking (AEB), Lane Departure Warning (LDW), Lane Keep Assist (LKA), Traffic Sign Recognition (TSR) and Automatic Headlight Control (AHL). Figure 1 RACam Front View. With an increase in Active Safety features in the automotive market there is also a corresponding increase in the complexity of the hardware which supports these safety features. Delphi’s hardware design for Active Safety has evolved over the years. In Delphi’s RACam product there are a number of critical components required in order to realize RADAR and Vision in a single package. RACam is also equipped with a fan and heater to improve the operating temperature range.
Design of a Robust Method and a System Architecture for Tracking Moving Vehicle under Noisy Radar Measurements
Abstract Innovation in the field of intelligent autonomous systems of the automotive sector has been ever increasing. Accurate tracking of vehicles is an important aspect in the design of applications such as smart route planning or collision avoidance systems. In practical applications, tracking of vehicle using radar technology suffers from serious problem due to noisy measurements. It introduces major limit on the accuracy of the tracking system. This paper discusses a case study scenario where the robustness of vehicle tracking can be improved using Extended Kalman Filtering. Noisy radar measurement is simulated through model based design (MBD) using MATLAB. Analysis and design of Extended Kalman Filter to mitigate the noise is discussed. An efficient system architecture to implement the algorithm in autonomous smart vehicle tracking system is also identified.
Abstract Based on RADAR and LiDAR measurements of deer with RADAR and LiDAR in the Spring and Fall of 2014 , we report the best fit statistical models. The statistical models are each based on time-constrained measurement windows, termed test-points. Details of the collection method were presented at the SAE World Congress in 2015. Evaluation of the fitness of various statistical models to the measured data show that the LiDAR intensity of reflections from deer are best estimated by the extreme value distribution, while the RCS is best estimated by the log-normal distribution. The value of the normalized intensity of the LiDAR ranges from 0.3 to 1.0, with an expected value near 0.7. The radar cross-section (RCS) varies from -40 to +10 dBsm, with an expected value near -14 dBsm.
The Use of Stationary Object Radar Sensor Data from Advanced Driver Assistance Systems (ADAS) in Accident Reconstruction
Abstract As a result of the development of Event Data Recorders (EDR) and the recent FMVSS regulation 49 CFR 563, today’s automobiles provide a limited subset of electronic data measurements of a vehicle’s state before and during a crash. Prior to this data, the only information available about the vehicle movements before or during a collision had come from physical evidence (e.g. tire marks), witnesses, aftermarket camera systems on vehicles, and ground-based cameras that were monitoring vehicle traffic or used for security surveillance. Today’s vehicles equipped with Advanced Driver Assistance Systems (ADAS) have vehicle-based sensors that measure information about the environment around a vehicle including other vehicles, pedestrians, and fixed wayside objects.
Abstract Aircrafts use Transponder for transmitting data to Air Traffic Control ground stations. Transponders automatically transmit a unique four-digit code when they receive a radio signal sent by radar. But when Transponder is shut down, and the redundant transponder fails to operate, there is no system within the aircraft which can continue transmitting altitude and important data to ATC ground stations. This has necessitated active research work to fundamentally design better and effective communication systems. At present, there is no evident redundant system to transponder unlike in case of Power-Plants, three-fold reliable, safety cum redundant power supply system are present. The present work introduces a novel design ‘RTSA’ which can be effective in catering safe transmission of emergency signal.
Aircraft In Situ Validation of Hydrometeors and Icing Conditions Inferred by Ground-based NEXRAD Polarimetric Radar
Abstract MIT Lincoln Laboratory is tasked by the U.S. Federal Aviation Administration to investigate the use of the NEXRAD polarimetric radars* for the remote sensing of icing conditions hazardous to aircraft. A critical aspect of the investigation concerns validation that has relied upon commercial airline icing pilot reports and a dedicated campaign of in situ flights in winter storms. During the month of February in 2012 and 2013, the Convair-580 aircraft operated by the National Research Council of Canada was used for in situ validation of snowstorm characteristics under simultaneous observation by NEXRAD radars in Cleveland, Ohio and Buffalo, New York. The most anisotropic and easily distinguished winter targets to dual pol radar are ice crystals.
Initial Results from Radiometer and Polarimetric Radar-based Icing Algorithms Compared to In-situ Data
In early 2015, a field campaign was conducted at the NASA Glenn Research Center in Cleveland, Ohio, USA. The purpose of the campaign is to test several prototype algorithms meant to detect the location and severity of in-flight icing (or icing aloft, as opposed to ground icing) within the terminal airspace. Terminal airspace for this project is currently defined as within 25 kilometers horizontal distance of the terminal, which in this instance is Hopkins International Airport in Cleveland. Two new and improved algorithms that utilize ground-based remote sensing instrumentation have been developed and were operated during the field campaign. The first is the ‘NASA Icing Remote Sensing System’, or NIRSS. The second algorithm is the ‘Radar Icing Algorithm’, or RadIA.
Abstract Nowadays active collision avoidance has become a major focus of research, and a variety of detection and tracking methods of obstacles in front of host vehicle have been applied to it. In this paper, laser radars are chosen as sensors to obtain relevant information, after which an algorithm used to detect and track vehicles in front is provided. The algorithm determines radar's ROI (Region of Interest), then uses a laser radar to scan the 2D space so as to obtain the information of the position and the distance of the targets which could be determined as obstacles. The information obtained will be filtered and then be transformed into cartesian coordinates, after that the coordinate point will be clustered so that the profile of the targets can be determined. A threshold will be set to judge whether the targets are obstacles or not. Last Kalman filter will be used for target tracking. To verify the presented algorithm, related experiments have been designed and carried out.
Abstract To reduce the number and severity of accidents, automakers have invested in active safety systems to detect and track neighboring vehicles to prevent accidents. These systems often employ RADAR and LIDAR, which are not degraded by low lighting conditions. In this research effort, reflections from deer were measured using two sensors often employed in automotive active safety systems. Based on a total estimate of one million deer-vehicle collisions per year in the United States, the estimated cost is calculated to be $8,388,000,000 . The majority of crashes occurs at dawn and dusk in the Fall and Spring . The data includes tens of thousands of RADAR and LIDAR measurements of white-tail deer. The RADAR operates from 76.2 to 76.8 GHz. The LIDAR is a time-of-flight device operating at 905 nm. The measurements capture the deer in many aspects: standing alone, feeding, walking, running, does with fawns, deer grooming each other and gathered in large groups.
Abstract Automotive radar and Vehicle to Vehicle (V2V) technology are currently being developed focusing in the safety of the drivers and passengers. The U.S. Department of Transportation's National Highway Traffic Safety Administration (NTHSA) announced that it is going to create a formal path forward for vehicle-to-vehicle communication for light vehicles meaning that NTHSA will start regulatory proposals on how this technology could become mandatory in the future. Automotive short-range radar (SRR) uses the electromagnetic field distribution around a vehicle including reflection from other objects to detect obstacles. If the vehicle is moving the radars can warn the driver to possible impacts and even automatically trigger safety devices such as seat belts or air bags. One of the biggest challenges on the design of SRR is the high frequency of operation which makes it difficult the use numerical simulation due to the small wavelength, leading to electrical large models.
Abstract Radar Cross Section (RCS) is the equivalent effective area of a given target intercepting a radar wave. In other words, RCS is a measure of how detectable a solid is with radar. For the past years, several electromagnetic numerical codes were used to calculate the RCS of aircrafts including the well known and commonly used Finite Element Method (FEM), Finite Difference Time Domain (FDTD) and Method of Moments (MoM). An incident planar wave is used to simulate the radar signal. Today a hybrid method known as Finite Element Boundary Integral (FEBI) solves a RCS model using the advantages of both FEM and MoM. This paper shows a series of RCS benchmarks listed in the literature comparing the results and performance of FEM, IE and FEBI. In order to show the state of the art of electromagnetic numerical codes and a more realistic analysis, several RCS of aircraft models are presented using FEBI and a true radar source.
Small and Lightweight Innovative Obstacle Detection Radar System for the General Aviation: Performances and Integration Aspects
Since 2011, ROD Ltd. and Boggi srl have started to cooperate in the field of airborne platform safety through the development and the integration of an innovative radar system, based on the radar system patented by in 2009 . ROD Ltd. is a startup company, created in 2011, in order to commercialize an innovative Obstacle and Terrain Avoidance Sensor concept (OTAS™). Boggi srl is an EASA DOA (21.J.453)  that has developed the capability of designing and certifying aerospace components from small changes to complex systems such as Remotely Piloted Air System (RPAS) or mission avionic. The direct experience of the operators in general aviation has shown that a number of accidents occur because of collisions with obstacles and, especially, but not only, with cables. During the years of 1997-2009, a total of 996 reported aviation accidents/collisions involving wires/power lines occurred in the United States. Of the 996 accidents, 301 involved at least one fatality .
This paper describes the development of a compact and low cost millimeter wave doppler radar sensor (77 GHz band), which can measure the vehicle ground speed precisely. The sensor has three unique features: First, all the radio frequency components are integrated into a single chip, including a millimeter wave transceiver and an on-chip antenna. Then, the chip package is made of plastic resin without use of expensive ceramic. Finally, a tiny dome-shaped resin lens is attached to the chip to collimate waves. These technologies enable the sensor to measure 53 x 71 x 65 mm₃, to weigh 115 grams. Compared to a conventional optical measuring instrument, for example, the sensor weighs only about fifteenth and is one-fifth of the size, while the measurement accuracy is almost comparable. So this sensor seems to have a variety of potential applications. In this paper, we also considered the feasibility of some other applications than just measuring ground speed.
In-flight Icing Hazard Verification with NASA's Icing Remote Sensing System for Development of a NEXRAD Icing Hazard Level Algorithm
From November 2010 until May of 2011, NASA's Icing Remote Sensing System was positioned at Platteville, Colorado between the National Science Foundation's S-Pol radar and Colorado State University's CHILL radar (collectively known as FRONT, or ‘Front Range Observational Network Testbed’). This location was also underneath the flight-path of aircraft arriving and departing from Denver's International Airport, which allowed for comparison to pilot reports of in-flight icing. This work outlines how the NASA Icing Remote Sensing System's derived liquid water content and in-flight icing hazard profiles can be used to provide in-flight icing verification and validation during icing and non-icing scenarios with the purpose of comparing these times to profiles of polarized moment data from the two nearby research radars.
In recent years the number of vehicles equipped with millimeter wave radar has been increasing due to the popularization of driving assistance systems such as adaptive cruise control (ACC) and forward vehicle collision warning (FCW) systems. Consequently, high performance millimeter wave radar must be developed to support even more advanced driving assistance systems. The investigation described in this paper confirms that it is possible to use high range resolution radar to recognize the width of a target. In tests, a simulated radar signal was transmitted and received by a millimeter waveband network analyzer using a 1.6 meter-wide aluminum foil board as the target. When the range resolution was low, only one point of reflection from the board could be detected. However, when the range resolution was improved, then multiple points of reflection from the target could be detected.
In the era of low cost product, enormous pressure to keep product development cost and time as low as possible. Durability verification and validation always consume big amount of product development cost and time. This paper gives low cost and quick, durability virtual verification which can be tested by running vehicle on track to check correlation. In this low cost durability verification, maximum stress levels on Frame and Cabin are generated analytically at all hot spot location based on finite element inertia relief analysis. Basic input load data is acquired by running proto type vehicle on track. Mathematical loading spectrum (Range Vs Cycle-Cumulative frequency distribution) for track is evaluated from acquired data. Stress spectrums are generated analytically for all hot spot locations based on mathematical load spectrum. Analytically component S-N curve for frame, cabin components with different slops [1,2] is generated based on material ultimate tensile stress values.
In-door simulation of Pass By Noise [PBN] Testing of a car was successfully attempted on a chassis dynamometer in a full-scale Vehicle Semi-Anechoic Chamber [VSAC]. The work has a practical approach for quick testing of vehicles to be submitted for certification. It has 3 parts: 1 Confirmation of overall Indoor PBN Testing as per ISO 362-1:2007 (E)2 Correlation of the PBN-results obtained on the Track with those in the VSAC as per both Method A and [proposed] Method B based on vehicle-acceleration depending on Power to Mass Ratio of the Test-vehicle3 Use of this In-door simulation for quick evaluation of design modifications of the vehicle to meet its PBN Limit with a safe margin Optimum no. of microphones was sought out in VSAC to reduce the set-up time without sacrificing accuracy of the results. Dyno-roller / tyre radiated noise need be reduced to have the close correlation with the Track results.
Edgewater's RTEdge™ Platform toolset is a model driven development environment for mission critical real-time systems. Using precise execution semantics and mathematical proof-based analysis, RTEdge™ enables the verification of critical properties of systems with high assurance. This case study will follow the design and implementation life-cycle of a system representing a real-world, mission critical domain: airborne electronic warfare. Using examples and constraints taken from this system, software components will be built to illustrate the principles of architectural conformance, timeliness and testing as executed within a static analysis framework. Using RTEdge™ as an example, this case study will introduce the concepts of model driven development in software and demonstrate how static analysis can be used to verify characteristics of a system that are traditionally left for later stages of development.
The Eaton VORAD Collision Warning System is utilized by many commercial trucking companies to improve and monitor vehicle and driver safety. The system is equipped with forward and side radar sensors that detect the presence and movements of vehicles around the truck to alert the driver of other vehicles' proximity. When the sensors detect that the host vehicle is closing on a vehicle ahead at a rate beyond a determined threshold, or that a nearby vehicle is located in a position that may be hazardous, the system warns the driver visually and audibly. The system also monitors parameters of the vehicle on which it is installed, such as the vehicle speed and turn rate, as well as the status of vehicle systems and controls. The monitored data is also recorded by the VORAD system and can be extracted in the event that the vehicle is involved in an accident.
The Sentinel-1 Mission is part of the Global Monitoring for Environment and Security (GMES) initiative whose overall objective is to support Europe's goals regarding sustainable development and global governance of the environment by providing timely and quality data, information, services and knowledge. The Sentinel-1 satellite is commissioned by ESA with Thales Alenia Space Italy as prime contractor and Astrium Germany as subcontractor for the Sentinel-1 SAR instrument. Sentinel-1 is an imaging radar mission at C-band aimed at providing continuity of data for user services. In particular, Sentinel-1 is aimed at providing data to the sea ice zones and the arctic environment, to surveillance of marine environment (wind speed, oil spill and ship detection) to monitoring and mapping land surfaces, and mapping in support of humanitarian aid in crisis situations.
Communication in Future Vehicle Cooperative Safety Systems: 5.9 GHz DSRC Non-Line-of-Sight Field Testing
Dedicated Short Range Communication (DSRC) is increasingly being recognized as the protocol of choice for vehicle safety applications by Original Equipment Manufacturers (OEMs) and road operators. DSRC offers the ability to communicate effectively from vehicle-to-vehicle and from vehicle to infrastructure with low latency and high reliability. A wide range of applications have been conceptualized to support safety, mobility and convenience, including: cooperative collision avoidance, travel information, and electronic payment. To be effective, infrastructure-based applications require an installed-vehicle base along with infrastructure deployment, while vehicle-to-vehicle applications require significant DSRC market penetration along with some degree of infrastructure support systems. Some vehicles currently include safety applications involving forward looking radar. The radar supplies information about objects, their distances and relative speed ahead of the host vehicle.
Automotive radar application is a focus in active traffic safety research activities. And accurate lateral position estimation from the leading target vehicle through radar is of great interest. This paper presents a method based on the regression tree, which estimates the rear centroid of leading target vehicle with a long range FLR (Forward Looking Radar) of limited resolution with multiple radar detections distributed on the target vehicle. Hours of radar log data together with reference value of leading vehicle's lateral offset are utilized both as training data and test data as well. A ten-fold cross validation is applied to evaluate the performance of the generated regression trees together with fused decision forest for each percentage of the training data.
Model of an Effective System for Dangerous Objects as a Contribution to Active Safety in Automotive Applications
Developments in electronics and mechanics have improved performance of vehicles in collision, especially during and after the crash, producing injuries and an economical impact to the owner of the vehicle. Lately several projects focused on preventing collision have raised as Active Safety Systems or so called in Europe, Advanced Driving Assistance Systems which have been developed facing the challenge of avoiding collisions. The goal of this project is to design, build and install a system capable of detecting and warning the driver about dangerous obstacles. In case the driver does not react on time the system will slow down the vehicle in order to decrease the collision velocity, or even avoid it. After a careful analysis of different LIDAR and non-vision passive infrared sensors are implemented and explored. This paper proposes a decision model using the combinations of some simple models of the driver, the vehicle, the control unit, and obstacle detection.
Increasing market penetration of driver assistance systems challenges system suppliers with ACC (Adaptive Cruise Control) and PSS (Predictive Safety Systems) functions with divergent requirements. This paper covers the technical development of a long range radar sensor that can address the requirements for high-performance systems as well as requirements for cost-efficient sensor components with robust and compact design and high quality standards, which are suited for high-volume production.
An obstacle recognition algorithm for the Pre-Crash Safety system has been newly developed with a stereo vision system and a millimeter wave radar with additional functions. This algorithm uses the merits of both the millimeter wave radar and the stereo vision system, and has two main features. One feature utilizes the merits of the stereo vision system detection with the detection results from the millimeter wave radar allowing for a more detailed horizontal position and width of the obstacle. This enables the equipment to operate at an earlier stage according to how well the relationship between the vehicle and the obstacle is understood. Another feature fuses detection from the millimeter wave radar and the stereo vision system. This system has succeeded in enhancing the detection performance of pedestrians who have been more difficult to detect than reflective objects such as cars.
In fatal accidents due to heavy duty trucks, the fatalities of occupants in passenger cars in which rear-end collision occur account for the largest percent. Collisions to the vehicles in traffic jams and collision to other accidents scenes on express ways can result in serious repercussions. Therefore the system which reduces the damage of collisions has long been demanded and here the world-first Pre-crash Safety (PCS) System for heavy duty trucks was developed. This system gives warning to the driver in case there is a possibility of collision with preceding vehicles, and activates the brakes to mitigate damage in case there is a higher possibility of collision. In order to get the maximum effect on the express ways where the trucks are in high speed, it is necessary to give warning and activate the brakes with relatively early timing.
Driver support systems without active vehicle interaction are convenience functions and can be viewed as a pre-stage to vehicle guidance and collision avoidance. By information to or an early warning of the driver a quicker reaction of the driver can be achieved. Today, Long Range Radar (LRR) and Lidar are used for the ACC function. With a range of up to 200m and superior signal quality, LRR is the key technology and main enabler for future predictive safety systems. Video technology has been introduced in a German Luxury class vehicle in 2005 for a system for night vision improvement. Also passive infrared sensing based thermal radiation sensors are used in other systems. Mid and short range applications are covered by various technologies based on Radar and optical sensing as well as on emerging new technologies. The highest demand regarding performance and reliability is put on active safety systems.
We propose a novel millimeter wave radar system and object detection algorithm for automobile use by using advanced null scanning method. Generally, null scanning method can achieve a higher resolution and a more compact sensor size compared to beam scanning method, but needs huge computing power. We introduced the theory of forgetting factor into it and developed a new null scan algorithm. It achieved a high lateral object separation ability of less than 3 degree, and a quick response under feasible computing power in simulation and test vehicle. These technologies enable compact and high performance radar for advanced safety system.