The driving environment perception has a vital role for autonomous driving and nowadays has been actively explored for its realization. The research community and relevant stakeholders necessitate the development of Deep Learning (DL) models and AI-enabled solutions to enhance autonomous vehicles (AVs) for smart mobility. There is a need to develop a model that accurately perceives multiple objects on the road and predicts the driver's perception to control the car's movements. This article proposes a novel utility-based analytical model that enables perception systems of AVs to understand the driving environment. The article consists of modules: acquiring a custom dataset having distinctive objects, i.e., motorcyclists, rickshaws, etc; a DL-based model (YOLOv8s) for object detection; and a module to measure the utility of perception service from the performance values of trained model instances. The perception model is validated based on the object detection task, and its process is benchmarked by state-of-the-art deep learning models' performance metrics from the nuScense dataset. The experimental results show three best-performing YOLOv8s instances based on mAP@0.5 values, i.e., SGD-based (0.832), Adam-based (0.810), and AdamW-based (0.822). However, the AdamW-based model (i.e., car: 0.921, motorcyclist: 0.899, truck: 0.793, etc.) still outperforms the SGD-based model (i.e., car: 0.915, motorcyclist: 0.892, truck: 0.781, etc.) because it has better class-level performance values, confirmed by the proposed perception model. We validate that the proposed function is capable of finding the right perception for AVs. The results above encourage using the proposed perception model to evaluate the utility of learning models and determine the appropriate perception for AVs.

Young motorcyclists, particularly those aged 15 to 24 years old, face a heightened risk of severe crashes due to factors such as speeding, traffic violations, and helmet usage. This study aims to identify key factors influencing crash severity by analyzing 10,726 young motorcyclist crashes in Texas from 2017 to 2022. Two advanced tabular deep learning models, ARMNet and MambaNet, were employed, using an advanced resampling technique to address class imbalance. The models were trained to classify crashes into three severity levels, Fatal or Severe, Moderate or Minor, and No Injury. ARMNet achieved an accuracy of 87 percent, outperforming 86 percent of Mambanet, with both models excelling in predicting severe and no injury crashes while facing challenges in moderate crash classification. Key findings highlight the significant influence of demographic, environmental, and behavioral factors on crash outcomes. The study underscores the need for targeted interventions, including stricter helmet enforcement and educational programs customized to young motorcyclists. These insights provide valuable guidance for policymakers in developing evidence-based strategies to enhance motorcyclist safety and reduce crash severity.

A "cocaine addicted" police officer who was found to be stealing drugs from an evidence store after he accidentally dropped a bag of white powder at his daughter's school has been jailed. Andrew Talbot, at the time a Greater Manchester Police detective, had taken just under 4kg (9lb) of cocaine worth almost 400,000 from police property rooms between 2018 and 2020. He also used the force's computer systems to find a drug dealer to help him sell the drugs on the streets of Manchester. The 54-year-old was found guilty of supplying the drug and misconduct in public office and sentenced to 19 years in jail at Liverpool Crown Court.GMPThe detective stole drugs from Greater Manchester's Police evidence rooms Sentencing him on Friday, Judge Neil Flewitt KC said Talbot had deceived colleagues to put a "significant" quantity of cocaine back into circulation as a result of his "addiction and greed". The investigation into Talbot by GMP's anti-corruption unit began in February 2020 after he dropped a small bag of cocaine outside his daughter's primary school.

We study the Knowledge-Based visual question-answering problem, for which given a question, the models need to ground it into the visual modality to find the answer. Although many recent works use question-dependent captioners to verbalize the given image and use Large Language Models to solve the VQA problem, the research results show they are not reasonably performing for multi-hop questions. Our study shows that replacing a complex question with several simpler questions helps to extract more relevant information from the image and provide a stronger comprehension of it. Moreover, we analyze the decomposed questions to find out the modality of the information that is required to answer them and use a captioner for the visual questions and LLMs as a general knowledge source for the non-visual KB-based questions. Our results demonstrate the positive impact of using simple questions before retrieving visual or non-visual information. We have provided results and analysis on three well-known VQA datasets including OKVQA, A-OKVQA, and KRVQA, and achieved up to 2% improvement in accuracy.

Increasing the number of cyclists, whether for general transport or recreation, can provide health improvements and reduce the environmental impact of vehicular transportation. However, the public's perception of cycling may be driven by the ideologies and reporting standards of news agencies. For instance, people may identify cyclists on the road as "dangerous" if news agencies overly report cycling accidents, limiting the number of people that cycle for transportation. Moreover, if fewer people cycle, there may be less funding from the government to invest in safe infrastructure. In this paper, we explore the perceived perception of cyclists within news headlines. To accomplish this, we introduce a new dataset, "Bike Frames", that can help provide insight into how headlines portray cyclists and help detect accident-related headlines. Next, we introduce a multi-task (MT) regularization approach that increases the detection accuracy of accident-related posts, demonstrating improvements over traditional MT frameworks. Finally, we compare and contrast the perceptions of cyclists with motorcyclist-related headlines to ground the findings with another related activity for both male- and female-related posts. Our findings show that general news websites are more likely to report accidents about cyclists than other events. Moreover, cyclist-specific websites are more likely to report about accidents than motorcycling-specific websites, even though there is more potential danger for motorcyclists. Finally, we show substantial differences in the reporting about male vs. female-related persons, e.g., more male-related cyclists headlines are related to accidents, but more female-related motorcycling headlines about accidents. WARNING: This paper contains descriptions of accidents and death.

Cleveland-born Samuel Alderson (1914-2005) created crash test dummies for the auto industry, drastically improving driver safety. Eleven additional people were killed in U.S. crashes involving vehicles that were using automated driving systems during a four-month period earlier this year, according to newly released government data, part of an alarming pattern of incidents linked to the technology. Ten of the deaths involved vehicles made by Tesla, though it is unclear from the National Highway Traffic Safety Administration's data whether the technology itself was at fault or whether driver error might have been responsible. The 11th death involved a Ford pickup truck. The deaths included four crashes involving motorcycles that occurred during the spring and summer: Two in Florida and one each in California and Utah.

Artificial Intelligence (AI) has changed the world for the better. AI and robotics have existed in fictional stories and movies for a very long time. They were shown both at good and bad lights. However, in the tech era, AI is unfolding its features to be a lifesaver. Everyone seems to be suddenly interested in AI.

Artificial intelligence continues to grow in use across products and services. The technology has touched nearly every aspect of business and society, from transforming hiring to making marketing more effective to increasing crop yields. But most importantly of all, AI is hard at work saving lives. Here are some of the amazing ways that AI is becoming the superhero of the tech world. AI is hard at work saving lives.

On October 19, a Waymo Pacifica struck and injured a motorcyclist in California. As is often the case, the collision was caused by a human - in this instance, the safety driver in the Waymo vehicle. In an unusual twist, however, Waymo CEO John Krafcik revealed that if the safety operator had not taken control of the autonomous minivan, then the self-driving software would have avoided a collision. Our simulation shows the self-driving system would have responded to the passenger car by reducing our vehicle's speed, and nudging slightly in our own lane, avoiding a collision." Waymo Autonomous Vehicle ("WaymoAV") was traveling at approximately 21 MPH westbound in Lane 2 of El Camino Real in Mountain View in self-driving mode. A passenger vehicle in Lane 1, to the left of the Waymo AV, began to change lanes into Lane2 to avoid a box truck blocking two lanes of traffic, Waymo's test driver took manual control of the AV out of an abundance of caution, disengaged from self-driving mode, and began changing lanes into Lane 3. A motorcycle, traveling at approximately 28 MPH behind the Waymo AV, had just entered Lane 3 to overtake the Waymo AV on its right. The motorcyclist reported injuries and was transported to the hospital for treatment. The Waymo AV sustained minor damage to the rear bumper."

A self-driving car that collided with and injured a motorcyclist was caused by the human back-up driver. Waymo, the autonomous car division of Google's parent firm Alphabet, revealed the human driver took control of the vehicle before crashing last month. According to Waymo's simulations after the accident, the car would have slowed down and avoided a collision if left to its own devices. Waymo has admitted the fault of the incident lies with the driver and not with its technology. Waymo, the autonomous car division of Google's parent firm Alphabet, revealed the human driver took control of the vehicle before crashing last month The unfortunate incident occurred when the driver felt the need to take control of the Waymo minivan and merge into the outside lane from the centre lane on the highway.