Active advanced arousal system to alert and avoid the crepuscular animal based vehicle collision

Animal Vehicle Collision (AVC) is relatively an evolving source of fatality resulting in the deficit of wildlife conservancy along with carnage. It’s a globally distressing and disturbing experience that causes monetary damage, injury, and human-animal mortality. Roadkill has always been atop the research domain and serendipitously provided heterogeneous solutions for collision mitigation and prevention. Despite the abundant solution availability, this research throws a new spotlight on wildlife-vehicle collision mitigation using highly efficient artificial intelligence during nighttime hours. This study focuses mainly on arousal mechanisms of the “Histogram of Oriented Gradients (HOG)” intelligent system with extracted thermography image features, which are then processed by a trained, convolutional neural network (1D-CNN). The above computer vision – deep learning-based alert system has an accuracy between 94%, and 96% on the arousal mechanisms with the empowered real-time data set utilization.

Driver’s Personality and Behavior for Boosting Automobile Security and Sensing Health Problems Through Fuzzy Signal Detection Case Study: Mexico City

Automobile security became an essential theme over the last years, and some automakers invested much money for collision avoidance systems, but personalization of their driving systems based on the user’s behavior was not explored in detail. Furthermore, efficiency gains could be had with tailored systems. In Mexico, 80% of automobile accidents are caused by human beings; the remaining 20% are related to other issues such as mechanical problems. Thus, 80% represents a significant opportunity to improve safety and explore driving efficiency gains. Moreover, when driving aggressively, it could be connected with mental health as a post-traumatic stress disorder. This paper proposes a Tailored Collision Mitigation Braking System, which evaluates the driver’s personality driving treats through signal detection theory to create a cognitive map that understands the driving personality of the driver. In this way, aggressive driving can be detected; the system is then trained to recognize the personality trait of the driver and select the appropriate stimuli to achieve the optimal driving output. As a result, when aggressive driving is detected continuously, an automatic alert could be sent to the health specialists regarding particular risky behavior linked with mental problems or drug consumption. Thus, the driving profile test could also be used as a detector for health problems.

A Predictive Frontal and Oblique Collision Mitigation System for Autonomous Vehicles

This paper presents a collision mitigation system for an unavoidable collision with an arbitrary oncoming obstacle vehicle. A set of candidate trajectories are generated by a model-based method and a hierarchical efficient collision-checking method is applied to check the potential collision between the predicted trajectory of the obstacle vehicle and the candidate trajectories of the ego vehicle. A novel method based on the vehicle polygon is applied to identify the specific impact location of the vehicle body. The predicted Delta-V and the identified impact location are combined to evaluate the outcome severity of the upcoming accident for each candidate trajectory. Based on the evaluated results, a path with the least damage would be selected and executed to mitigate the collision. Simulation and analysis are performed to investigate the performance of the presented system in a high-speed scenario of a detailed vehicle model.

PERPENDICULAR TURNING SOLAR TRACKER ANTI-COLLISION CAR

"We could definitely make a flying car - but that's not the hard part. The hard part is, how do you make a flying car that is super safe and quiet?” - ELON MUSK While safety, reliability, fuel economy, and low running costs put them at the top of the list of what people consider to be the 'most important' in a car, more than a third (36%) of those tested online rank with the latest driving skills in the same fields. Driving technology includes steering or parking assist, adaptive cruise control, and wireless entry or ignition. More than a quarter (28%) of people online also account for having the latest passenger technology, which includes audio or video streaming and social networking, as 'most important' to them. Our team has developed a vehicle archetype which is incorporated of Azimuth Angle, trimming off the turning radius and anti-collision mitigation system like concepts with a single touch button. The underlying concept of the smart car is to free the driver from many of the mundane tasks associated with driving, making the act of driving more pleasant.

Decentralized spectrum learning for radio collision mitigation in ultra-dense IoT networks: LoRaWAN case study and experiments

This paper describes the theoretical principles and experimental results of reinforcement learning algorithms embedded into IoT devices (Internet of Things), in order to tackle the problem of radio collision mitigation in ISM unlicensed bands. Multi-armed bandit (MAB) learning algorithms are used here to improve both the IoT network capability to support the expected massive number of objects and the energetic autonomy of the IoT devices. We first illustrate the efficiency of the proposed approach in a proof-of-concept, based on USRP software radio platforms operating on real radio signals. It shows how collisions with other RF signals are diminished for IoT devices that use MAB learning. Then we describe the first implementation of such algorithms on LoRa devices operating in a real LoRaWAN network at 868 MHz. We named this solution IoTligent. IoTligent does not add neither processing overhead, so it can be run into the IoT devices, nor network overhead, so that it requires no change to LoRaWAN protocol. Real-life experiments done in a real LoRa network show that IoTligent devices’ battery life can be extended by a factor of 2, in the scenarios we faced during our experiment. Finally we submit IoTligent devices to very constrained conditions that are expected in the future with the growing number of IoT devices, by generating an artificial IoT massive radio traffic in anechoic chamber. We show that IoTligent devices can cope with spectrum scarcity that will occur at that time in unlicensed bands.

Habitat Suitability Based Models for Ungulate Roadkill Prognosis

Roads do not only have a detrimental effect on nature (fragmenting habitats, isolating populations and threatening biodiversity), but the increasing numbers of wildlife-vehicle collisions are also a direct threat to humans and property. Therefore, mitigation measures should be placed with respect to animal distribution and movements across the roads. We simulated red deer, roe deer and wild boar movements in Lithuania, focusing on the two main highways A1 and A2. Using regional habitat suitability and linkage models, we calculated movement pathways and the most probable crossing zones in 2009. The prognostic value of these models was tested by comparing the pathway predictions to the real roadkill and roadkill cluster locations in 2002–2009 and 2010–2017. Across both periods and on both highways, the roe deer roadkill locations were significantly closer to the model-predicted pathways than to randomly selected points. The prediction of roadkill locations was also good for wild boar. The roe deer roadkill clusters and multi-species clusters were significantly better represented by the model than by random distribution. On both highways, the biggest differences in distance from the predicted locations were near big cities. We recommended wildlife movement models as an additional tool for planning wildlife-vehicle collision mitigation measures and we advise measures for increasing their predicting power.