Browse Topic: Collision warning systems
Bird accidental collision with overhead transmission lines poses a threat to the ecology of rare bird populations. This article analyzes the warning measures to prevent birds from accidental collisions at home and abroad. In response to the low efficiency of manual installation and the poor static warning effect in preventing birds from accidental collisions with overhead transmission lines, the visual characteristics of birds are analyzed. A drone-based automatic installation flash-type bird accidental collision warning device is proposed, which includes a fixture, a disc, and a luminous circuit. The fixture can be carried and installed on the overhead line by a drone and can be easily disassembled. The disc adopts eye-catching colors and has a hollow structure to reduce wind resistance load. The luminous circuit includes solar panels, charge and discharge control circuits, flicker control circuits, batteries, and luminous components. The drone suspension warning device test was conducted, and the results showed that the device can be easily suspended from the overhead line by the drone.
Avoiding and mitigating any potential collision is dependent on (1) road user ability to avoid entering into a conflict (conflict avoidance effect) and (2) road user response should a conflict be entered (collision avoidance effect). This study examined the collision avoidance effect of the Waymo Driver, a currently deployed SAE level 4 automated driving system (ADS), using a human behavior reference model, designed to be representative of a human driver that is non-impaired, with eyes on the conflict (NIEON). Reliable performance benchmarking methodologies for assessing ADS performance are an essential component of determining system readiness. This consistently performing, always-attentive driver does not exist in the human population. Counterfactual simulations were run on responder collision scenarios based on reconstructions from a 10-year period of human fatal crashes from the Operational Design Domain of the Waymo ADS in Chandler, Arizona. Of 16 simulated conflicts entered, 12 (75%) were prevented by the Waymo Driver, and 10 (62.5%) were prevented by the NIEON model. The NIEON Model mitigated an additional 5 collisions and did not mitigate 1 collision. In these 16 conflicts entered, 93% of serious injury risk was reduced by the Waymo Driver, whereas 84% of serious injury risk was reduced by the NIEON model. Further, in a case-by-case evaluation, the Waymo Driver’s collision avoidance led to reduced serious injury risk when compared to the NIEON model in every simulated event. The results of this paper demonstrate that a reference model like NIEON can be used to benchmark ADS responder performance in response to high-risk initiating behaviors performed by the current driving population.
Advanced Driver Assistance Systems (ADAS) have become increasingly prevalent in modern vehicles, promising improved safety and reducing accidents. However, their implementation comes with several challenges and limitations. The efficacy of these systems in diverse and challenging road conditions of India, remains as a concern. For deeper understanding of the ADAS feature related concerns in Indian market due to the factors such as unique road conditions, traffic situations, driving patterns, an extensive study was done throughout Indian terrain. The functionality and performance of different ADAS features were evaluated in the real-world scenarios. The objective data of the observations and occurrence conditions were captured with help of data loggers & camera setups inside the vehicle. This research paper represents a comprehensive study on the challenges faced by user while using ADAS enabled cars in Indian road conditions. We captured the performance data of various ADAS features, including lane departure warning (LDW), lane keep assist (LKA), adaptive cruise control (ACC), automatic emergency braking (AEB), Forward Collision Warning (FCW) and blind spot monitoring (BSM) in real-world driving scenarios and assessed the functionality and limitations of various ADAS features. Study shows that ADAS can significantly enhance road safety, however in certain cases effectiveness is compromised by factors such as poor road infrastructure, inadequate lane markings, unpredictable driver behavior etc. The result of the study highlights the challenges and give insights to develop the ADAS for Indian market scenarios and gives opportunity to auto OEMs for making test cases as per Indian road & driving conditions.
Traditionally, occupant safety research has centered on passive safety systems such as seatbelts, airbags, and energy-absorbing vehicle structures, all designed under the assumption of a nominal occupant posture at the moment of impact. However, with increasing deployment of active safety technologies such as Forward Collision Warning (FCW) and Autonomous Emergency Braking (AEB), vehicle occupants are exposed to pre-crash decelerations that alter their seated position before the crash. Although AEB mitigates the crash severity, the induced occupant movement leads to out-of-position behavior (OOP), compromising the available survival space phase and effectiveness of passive restraint systems during the crash. Despite these evolving real-world conditions, global regulatory bodies and NCAP programs continue to evaluate pre-crash and crash phases independently, with limited integration. Moreover, traditional Anthropomorphic Test Devices (ATDs) such as Hybrid III dummies, although highly repeatable, lack the bio-fidelity necessary to capture human-like kinematics during pre-crash braking events involving low g. ATDs do not simulate the spinal articulation, posture adjustments and active muscle contraction that occur during emergency maneuvers or pre-crash scenarios. To overcome these limitations, researchers have increasingly turned to Human Body Models (HBMs) such as Total Human Model for Safety (THUMS) and Global Human Body Model Consortium (GHBMC). These models enable high-fidelity finite element (FE) simulations with anatomical realism, allowing for the inclusion of active musculature and posture changes. This study aims to quantify the occupant forward excursion under pre-crash phase (due to AEB) and explore the possibility of an integrated simulation framework that evaluates occupant safety across both pre-crash and crash events. For this, the approach was to carry out full vehicle braking tests (1g braking pulse) with adult male (AM50) volunteers at different speeds to measure forward head excursion during pre-crash. These scenarios were replicated in LS-Dyna using THUMS HBM, showing strong agreement with experimental data. The resulting excursed postures were then used in crash simulations with ATDs to evaluate the effect on injury outcomes. Overall, the findings demonstrate effect of forward excursion on occupant injuries and the effectiveness of HBMs in capturing occupant kinematics, during pre-crash events.
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 completion of the driving activity, the system activated events: single event (only FCW) or combined event (FCW + AEB), and no activation events were analyzed. Three evaluators classified each system activated event as either a true positive or a false positive. Further, no activation events, where the driver felt the FCW should but did not activate, were classified as false negatives. A total of 79 single and combined events were identified. The AEB system produced 2.9 true positives and 0.2 false positives per 1000 km, while the FCW system produced 6.3 true positives and 2.1 false positives per 1000 km. For the FCW system, there were also 0.3 false negatives per 1000 km. The inter-rater reliability for the three evaluators was moderate indicating that not enough data is provided to accurately understand and classify the events. Reliable performance evaluation with the chosen simple approach seems highly challenging.
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.
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 bicycles to get voice-assisted warnings when vehicles are dangerously close to them. The data from a fleet of such systems can be collated through a bundled mobile app so that each route in a city can be rated based on the number of close calls/cyclist related accidents per day which gives a safety score for the route allowing riders to choose the safest route and the best time to ride. This whole ecosystem is envisioned as an open source, easy to setup template that any rider/group of riders can use or adapt to their use case with minimum technical knowledge.
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.
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