Browse Topic: Collision intervention systems
ABSTRACT In any active safety system, it is desired to measure the “performance”. For the estimation case, generally a cost function like Mean-Square Error is used. For detection cases, the combination of Probability of Detection and Probability of False Alarm is used. Scenarios that would really expose performance measurement involve complex, dangerous and costly driving situations and are hard to recreate while having a low probability of actually being acquired . Using a virtual tool, we can produce the trials necessary to adequately determine the performance of active safety algorithms and systems. In this paper, we will outline the problem of measuring the performance of active safety algorithms or systems. We will then discuss the approach of using complex scenario design and Monte Carlo techniques to determine performance. We then follow with a brief discussion of Prescan and how it can help in this endeavor. Finally, two Monte Carlo type examples for particular active safety
New tests for a Truck Safe rating scheme aim to emulate real-world collisions and encourage OEMs to fit collision avoidance technologies and improve driver vision. Euro NCAP has revealed the elements it is considering as part of an upcoming Truck Safe rating, and how it intends to test and benchmark truck performance. The announcement was made to an audience of international road safety experts at the NCAP24 World Congress in Munich, Germany, in April. The action is intended to mitigate heavy trucks' impact on road safety. The organization cited data showing that trucks are involved in almost 15% of all EU road fatalities but represent only 3% of vehicles on Europe's roads. Euro NCAP says the future rating scheme is designed to go further and faster than current EU truck safety regulations. The organization's goal is to drive innovation and hasten the adoption of advanced driver-assistance systems (ADAS) such as automatic emergency braking (AEB) and lane support systems (LSS), while
From the past few years, there is a pressing need for implementation of automatic in-vehicle safety systems to avoid vehicle crashes and fatalities. Development of autonomous emergency braking systems (AEBS) to detect and avoid collisions in such critical moments is of paramount importance. In this paper, AEBS is developed for a four-wheeler system that aims to detect vehicles and controls the ego vehicle based on the expected stooping distance (ESD). This control system aims to react based on the real-time relative distance & speed of the ego vehicle to actuate appropriate braking force. Control systems developed in Altair Activate are co-simulated with CARLA, a virtual reality simulator for autonomous driving research. Various scenarios including low and high-speed car to car motion, urban high and low traffic density environments are simulated to study the robustness of the control system. Further, studies are conducted to evaluate the effectiveness of the systems by varying the
Autonomous Emergency Braking (AEB) systems play a critical role in ensuring vehicle safety by detecting potential rear-end collisions and automatically applying brakes to mitigate or prevent accidents. This paper focuses on establishing a framework for the Verification & Validation (V&V) of Advanced Driver Assistance Systems (ADAS) by testing & verifying the functionality of a RADAR-based AEB ECU. A comprehensive V&V approach was adopted, incorporating both virtual and physical testing. For virtual testing, closed-loop Hardware-in-Loop (HIL) simulation technique was employed. The AEB ECU was interfaced with the real-time hardware via CAN. Data for the relevant target such as the target position, velocity etc. was calculated using an ideal RADAR sensor model running on the real-time hardware. The methodology involved conducting a series of test scenarios, including various driving speeds, obstacle types, and braking distances. Automation was leveraged to perform automated testing and
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
Provizio promises its 5D Perception stack can safely compete with expensive lidar sensors at a fraction of the cost. “Safety first” is more than a catchphrase. For sensing company Provizio, it's the only way the transportation industry should introduce autonomous vehicles. In Provizio's view, using AV building blocks - technology such as automatic emergency braking and lane-keep assist - can be valuable in ADAS systems, but they should not be used to drive vehicles until the perception problem has been solved. “It's not that we're skeptical about autonomous driving, it's just that we strongly believe that the industry has taken this wrong path,” Dane Mitrev, machine learning engineer at Provizio, told SAE Media at September 2023's AutoSens Brussels conference. “The industry has looked at things the other way around. They tried to solve autonomy first, without looking at accident prevention and simpler ADAS systems. We are building a perception technology which will first eliminate road
In autonomous driving vehicles with an automation level greater than three, the autonomous system is responsible for safe driving, instead of the human driver. Hence, the driving safety of autonomous driving vehicles must be ensured before they are used on the road. Because it is not realistic to evaluate all test conditions in real traffic, computer simulation methods can be used. Since driving safety performance can be evaluated by simulating different driving scenarios and calculating the criticality metrics that represent dangerous collision risks, it is necessary to study and define the criticality metrics for the type of driving scenarios. This study focused on the risk of collisions in the confluence area because it was known that the accident rate in the confluence area is much higher than on the main roadway. There have been several experimental studies on safe driving behaviors in the confluence area; however, there has been little study logically exploring the merging
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
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