Browse Topic: Collision warning systems
ABSTRACT The Army has identified an operational need for a Robotic Convoy capability for its tactical vehicle fleets. The Department of Defense (DoD), with a fleet of over several hundred thousand tactical vehicles, must identify an approach with supporting technology and supply base to procure and support a Robotic Convoy solution at the lowest possible cost. While cost is a key driver, the selected system approach must be proven and robust to ensure the safety of our soldiers and the supply chain. An effective approach is to integrate and adapt the advanced automotive technologies, components and suppliers currently delivering advanced safety technologies into the automotive market. These advanced automotive technologies merged with DoD robotics enhancements in tactical behaviors, autonomous driving, command & control and unmanned systems collaboration will advance the operational utility of robotic convoy application in manned and unmanned modes. Figure 1 Military Application The
Road traffic fatalities in India have been increasing, reaching around 150,000 fatalities a year. To reduce fatalities, some prospective studies suggested using active safety technologies such as Forward Collision Warning (FCW), and Autonomous Emergency Braking (AEB). However, the effectiveness of FCW and AEB on Indian roads using retrospective studies is not known. Vehicle data such as radar, and controller area network signals could be used for the evaluation of the systems (FCW and AEB). However, these data are not readily accessible. This exploratory study aims to explore the opportunities and limitations of using simple dashboard cameras for a Field Operational Test. One European car with state-of-the-art FCW and AEB systems was rented. Fifteen drivers shared the vehicle, driving almost 10,000 km over 29 days. The vehicle was mounted with a set of dashboard cameras. The navigator noted the “system activated” events and “no activation” events in the logbook during the drive. Post
This SAE Information Report provides a compendium of terms, definitions, abbreviations, and acronyms to enable common terminology for use in engineering reports, diagnostic tools, and publications related to active safety systems. This information report is a survey of active safety systems and related terms. The definitions offered are descriptions of functionality rather than technical specifications. Included are warning and momentary intervention systems, which do not automate any part of the dynamic driving task (DDT) on a sustained basis (SAE Level 0 as defined in SAE J3016), as well as definitions of select features that perform part of the DDT on a sustained basis (SAE Level 1 and 2
ADAS and HMI development are new applications for simulation solutions. The concept of designing, engineering and manufacturing a new vehicle without physical prototypes is typically viewed as either impractical or mythical. Even as virtual development processes have become increasingly capable, experts maintain that hard prototypes are still needed to validate the fidelity of virtual models. But “zero prototypes” is more than a slogan at one of the top providers of real-time simulation and driving simulator solutions. For VI-grade, zero prototypes are a crusade
This Recommended Practice, Operational Definitions of Driving Performance Measures and Statistics, provides functional definitions of and guidance for performance measures and statistics concerned with driving on roadways. As a consequence, measurements and statistics will be calculated and reported in a consistent manner in SAE and ISO standards, journal articles proceedings papers, technical reports, and presentations so that the procedures and results can be more readily compared. Only measures and statistics pertaining to driver/vehicle responses that affect the lateral and longitudinal positioning of a road vehicle are currently provided in this document. Measures and statistics covering other aspects of driving performance may be included in future editions. For eye glance-related measures and statistics, see SAE J2396 (Society of Automotive Engineers, 2007) and ISO 15007-1 (International Standards Organization, 2002
This SAE Recommended Practice (RP) establishes uniform powered vehicle-level test procedure for forward collision warning (FCW) and automatic emergency braking (AEB) used in trucks and buses greater than 10000 pounds (4535 kg) GVWR equipped with pneumatic brake systems for detecting, warning, and avoiding potential collisions. This RP does not apply to electric powered vehicles, trailers, dollies, etc., and does not intend to exclude any particular system or sensor technology. These FCW/AEB systems utilize various methodologies to identify, track, and communicate data/information to the operator and vehicle systems to warn, intervene, and/or mitigate in the momentary longitudinal control of the vehicle. This specification will test the functionality of the FCW/AEB (e.g., ability to detect objects in front of the vehicle), its ability to indicate FCW/AEB engagement and disengagement, the ability of the FCW/AEB to notify the human machine interface (HMI) or vehicle control system that an
The urban traffic in India is more chaotic than ever. The pandemic saw more people adopting cycling as a recreational as well as a healthier and eco-friendly means of commute. The road infrastructure and driving culture in the country are not “cyclist-friendly”, making cyclists more vulnerable than a pedestrian. With an increasing number of beginner cyclists, there is a higher risk of other vehicles shunting them. Although many rider assistance safety solutions exist, they are mostly in their experimental stages, very far from a commercial release. These systems are often expensive as they are early production prototypes which makes them less accessible to the public. This work tries to propose a simple, efficient, and easy-to-make active safety system for cyclists that will act as a third eye. The system relies on low-cost stereo cameras, edge-computing modules, artificial intelligence, and A-GPS to create an active warning system for cyclists, which can be mounted on the back of the
This document provides a summary of the activities to-date of Task Force #1 - Research Foundations – of the SAE’s Driver Vehicle Interface (DVI) committee. More specifically, it establishes working definitions of key DVI concepts, as well as an extensive list of data sources relevant to DVI design and the larger topic of driver distraction
In advanced driver assistance systems (ADAS) or autonomous driving Systems (ADS) the robust and reliable perception of the environment, especially for the detecting and tracking the surrounding vehicle is prerequisite for collision warning and collision avoidance. In this paper a post-fusion tracking approach is presented which combines the front view Radar observation and front smart camera information. The approach can improve the tracking accuracy of the tracking system to support ADAS or ADS function such as adaptive cruise control (ACC) or autonomous emergency braking (AEB). The paper describes the state estimation algorithm, data association in the fusion architecture. Furthermore, the fusion architecture is tested and validated in real highway driving scenario
Pedestrian passive safety and active safety both develop rapidly, such as new structural hoods/airbags for pedestrian protection and emergency automatic braking/forward collision warning are used in advanced driver assistance system (ADAS). In this study, improved pedestrian passive safety is to obtain optimal hood structural parameters and add an active pop-up hood. Headform impactor, hood model, simplified vehicle and head impaction models were established, and nine key test points were selected for crash simulation tests. After the simulation, the pedestrian protection performance of the initial hood is evaluated and analyzed based on the head injury criterion (HIC) values. Combined with the orthogonal experimental design method, this study acquired the best structural parameters scheme and applied to the active pop-up hood. The validation results show that after applying the optimal structural parameters to the active pop-up hood, the pedestrian protection performance of the hood
Adaptive cruise control (ACC) is an enhancement of conventional cruise control systems that allows the ACC-equipped vehicle to follow a forward vehicle at a pre-selected time gap, up to a driver selected speed, by controlling the engine, power train, and/or service brakes. This SAE Standard focuses on specifying the minimum requirements for ACC system operating characteristics and elements of the user interface. This document applies to original equipment and aftermarket ACC systems for passenger vehicles (including motorcycles). This document does not apply to heavy vehicles (GVWR > 10,000 lbs. or 4,536 kg). Furthermore, this document does not address other variations on ACC, such as “stop & go” ACC, that can bring the equipped vehicle to a stop and reaccelerate. Future revisions of this document should consider enhanced versions of ACC, as well as the integration of ACC with Forward Vehicle Collision Warning Systems (FVCWS
This SAE Technical Information Report provides a compendium of terms, definitions, abbreviations, and acronyms to enable common terminology for use in engineering reports, diagnostic tools, and publications related to active safety systems. This information report is a survey of active safety systems and related terms. The definitions offered are descriptions of functionality rather than technical specifications. Included are warning and momentary intervention systems, which do not automate any part of the dynamic driving task (DDT) on a sustained basis (SAE Level 0 as defined in SAE J3016), as well as definitions of select features that perform part of the DDT on a sustained basis (SAE Level 1 and 2
Given the current proliferation of active safety features on new vehicles, especially for Advanced Driver Assistance Systems (ADAS) and Highly Automated Driving (HAD) technologies, it is evident that there is a need for testing methods beyond a vehicle level physical test. This paper will discuss the current state of the art in the industry for simulation-based verification and validation (V&V) testing methods. These will include, but are not limited to, "Hardware-in-the-Loop (HIL)", “Software-in-the-Loop (SIL)”, “Model-in-the-Loop (MIL)”, “Driver-in-the-Loop (DIL)”, and any other suitable combinations of the aforementioned (XIL). Aspects of the test processes and needed components for simulation will be addressed, detailing the scope of work needed for various types of testing. The paper will provide an overview of standardized test aspects, active safety software validation methods, recommended practices and standards. The focus will be on trade-offs of cost and performance, as well
The bus sector is currently lagging behind when it comes to implementing autonomous systems for improved vehicle safety. However, in cities such as London, public transport strategies are changing, with requirements being made for advanced driver-assistance systems (ADAS) on buses. This study discusses the adoption of ADAS systems within the bus sector. A review of the on-road ADAS bus trials shows that passive forward collision warning (FCW) and intelligent speed assistance (ISA) systems have been successful in reducing the number of imminent pedestrian/vehicle collision events and improving speed limit compliance, respectively. Bus accident statistics for Great Britain have shown that pedestrians account for 82% of all fatalities, with three quarters occurring with frontal bus impacts. These statistics suggest that the bus forward collision warning system is a priority for inclusion in future vehicles to enhance the driver’s direct vision, and to increase reaction time for earlier
The high popularity of automobiles has led to frequent collisions. According to the latest statistics of the United Nations, about 1.25 million people worldwide die from road traffic accidents each year. In order to improve the safety of vehicles in driving, the active safety system has become a research hotspot of various car companies and research institutions around the world. Among them, the more mature and popular active security system are Forward Collision Warning(FCW) and Autonomous Emergency Braking(AEB). However, the current active safety system is based on traditional sensors such as radar and camera. Therefore, the system itself has many limitations due to the shortage of traditional sensors. Compared to traditional sensors, Vehicle to Everything (V2X) technology has the advantages of richer vehicle parameter information, no perceived blind spots, dynamic prediction of dangerous vehicle status, and no occlusion restriction. In order to overcome the many shortcomings of the
Forward collision warning is one of the most challenging concerns in the safety of autonomous vehicles. A cooperation between many sensors such as LIDAR, Radar and camera helps to enhance the safety. Apart from the importance of having a reliable object detector, the safety system should have requisite capabilities to make reasonable decisions in the moment. In this work, we concentrate on detecting front vehicles of autonomous cars using a monocular camera, beyond only a detection method. In fact, we devise a solution based on a cooperation between a deep object detector and a reinforcement learning method to provide forward collision warning signals. The proposed method models the relation between acceleration, distance and collision point using the area of the bounding box related to the front vehicle. An agent of learning automata as a reinforcement learning method interacts with the environment to learn how to behave in eclectic hazardous situations. The agent follows a
Vehicle manufacturers are beginning to improve existing autonomous emergency braking (AEB) algorithms by adding pedestrian identification and avoidance capability. The Insurance Institute for Highway Safety (IIHS) has performed tests on eleven such vehicles; data are publicly available and were analyzed for this study. The first objective of this study was to compare Forward Collision Warning (FCW) engagement distance to target, pedestrian automatic emergency braking (P-AEB) brake application time, and incidences of impact across different manufacturers. It was observed that there exists a wide variation in FCW and AEB performance across manufacturers. FCW engagement distance tended to increase with test speed. Time from FCW engagement to AEB engagement was usually less than one second, with some manufacturer-specific variation. Incidences of impact tended to increase with travel speed, although some vehicles tested maintained constant number of incidences of impacts at all speeds
In this paper, a CFAR detection algorithm based on sorting selection is proposed for the vehicle millimeter wave radar in the actual detection. The principle of this algorithm is derived from the mean class CFAR and the ordered selection class (OS) CFAR algorithm. First, CA-CFAR and SO-CFAR are simulated and detected in the presence of extended range targets, and it is found that the detection performance can be improved by changing the protection unit. At the same time, the proposed method was tested and compared under the same conditions. Results show that although the detection performances of CA and SO-CFAR can be improved by increasing the number of protection units, they are not suitable for practical applications. However, the proposed method not only has no need for protection units but also has better detection performance. Then, CA-CFAR, SO-CFAR and the new algorithm are verified and compared using the real data obtained by a stationary vehicle. Results show that the
Advance Active Safety Systems play a preventive role in mitigating crashes and accidents by providing warning, additional assistance to the driver and maneuverability of vehicle by itself. Some of the features include forward collision warning system and lane departure warning system activate a warning alert when potentially dangerous situations are detected. These active safety features present in developed markets work with Fusion based algorithm combining Radar, Lidar, Camera, Ultrasonic sensor’s input. Application of these algorithms are Intelligent Cruise Control, Collision avoidance, parking assistance, identify pedestrian etc. The complexity of the algorithm, cost of the control unit and road infrastructure are hindrance to emerging market. The solution presented in this paper is towards camera-based solution, describing the method to determine the predictive path, that is obstacle free space and use the predictive space to navigate or steer. This paper focuses on vehicle
This SAE Standard describes standardized medium-independent messages needed by information service providers for Advanced Traveler Information Systems (ATIS). The messages contained herein address all stages of travel (informational, pre-trip and en route), all types of travelers (drivers, passengers, personal devices, computers, other servers), all categories of information, and all platforms for delivery of information (in-vehicle, portable devices, kiosks, etc
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