Road traffic accidents pose a significant global public health concern, leading to injuries, fatalities, and vehicle damage. Approximately 1,3 million people lose their lives daily due to traffic accidents [World Health Organization, 2022]. Addressing this issue requires accurate traffic law violation detection systems to ensure adherence to regulations. The integration of Artificial Intelligence algorithms, leveraging machine learning and computer vision, has facilitated the development of precise traffic rule enforcement. This paper illustrates how computer vision and machine learning enable the creation of robust algorithms for detecting various traffic violations. Our model, capable of identifying six common traffic infractions, detects red light violations, illegal use of breakdown lanes, violations of vehicle following distance, breaches of marked crosswalk laws, illegal parking, and parking on marked crosswalks. Utilizing online traffic footage and a self-mounted on-dash camera, we apply the YOLOv5 algorithm's detection module to identify traffic agents such as cars, pedestrians, and traffic signs, and the strongSORT algorithm for continuous interframe tracking. Six discrete algorithms analyze agents' behavior and trajectory to detect violations. Subsequently, an Identification Module extracts vehicle ID information, such as the license plate, to generate violation notices sent to relevant authorities.

Self-driving cars and trucks, autonomous vehicles (AVs), should not be accepted by regulatory bodies and the public until they have much higher confidence in their safety and reliability -- which can most practically and convincingly be achieved by testing. But existing testing methods are inadequate for checking the end-to-end behaviors of AV controllers against complex, real-world corner cases involving interactions with multiple independent agents such as pedestrians and human-driven vehicles. While test-driving AVs on streets and highways fails to capture many rare events, existing simulation-based testing methods mainly focus on simple scenarios and do not scale well for complex driving situations that require sophisticated awareness of the surroundings. To address these limitations, we propose a new fuzz testing technique, called AutoFuzz, which can leverage widely-used AV simulators' API grammars to generate semantically and temporally valid complex driving scenarios (sequences of scenes). To efficiently search for traffic violations-inducing scenarios in a large search space, we propose a constrained neural network (NN) evolutionary search method to optimize AutoFuzz. Evaluation of our prototype on one state-of-the-art learning-based controller, two rule-based controllers, and one industrial-grade controller in five scenarios shows that AutoFuzz efficiently finds hundreds of traffic violations in high-fidelity simulation environments. For each scenario, AutoFuzz can find on average 10-39% more unique traffic violations than the best-performing baseline method. Further, fine-tuning the learning-based controller with the traffic violations found by AutoFuzz successfully reduced the traffic violations found in the new version of the AV controller software.

Police officer presence at an intersection discourages a potential traffic violator from violating the law. It also alerts the motorists' consciousness to take precaution and follow the rules. However, due to the abundant intersections and shortage of human resources, it is not possible to assign a police officer to every intersection. In this paper, we propose an intelligent and optimal policing strategy for traffic violation prevention. Our model consists of a specific number of targeted intersections and two police officers with no prior knowledge on the number of the traffic violations in the designated intersections. At each time interval, the proposed strategy, assigns the two police officers to different intersections such that at the end of the time horizon, maximum traffic violation prevention is achieved. Our proposed methodology adapts the PROLA (Play and Random Observe Learning Algorithm) algorithm [1] to achieve an optimal traffic violation prevention strategy . Finally, we conduct a case study to evaluate and demonstrate the performance of the proposed method.

It's found just the right place in Arizona, which has continued its lax regulatory policy for autonomous vehicles after an Uber self-driving Volvo killed a pedestrian on March 18 in Tempe. Nuro's co-founder and president, David Ferguson, signed a registration letter to the state Department of Transportation for the company on April 17, confirming that it planned to start testing fully autonomous vehicles on Arizona roads. On March 1, Governor Doug Ducey published an executive order to address fully autonomous vehicles, adding a modicum of oversight to his pro-business policy. All companies that intend to put fully driverless vehicles on Arizona roads in the near future, or are already testing them on roads, were ordered to register with the state within 60 days. As of May 2, Nuro was one of only two companies that had filed the required statement.

"The computer is fallible, so it's the human who is supposed to be perfect," one former Uber test driver said. "It's kind of the reverse of what you think about computers." The fatal crash last week in Tempe, Ariz., involving an Uber autonomous vehicle is bringing new scrutiny to both the quality of Uber's technology for avoiding collision and the efficacy of its backup system of so-called safety drivers. The accident, in which a woman was struck and killed as she walked a bicycle across a road at night, is believed to be the first involving a death from a self-driving car. In much of the autonomous-vehicle testing done on public roads, there are two safety drivers: one in the driver's seat; and one in the front passenger seat who is assigned the task of logging incidents onto a computer, but, drivers say, also helps by keeping a second set of eyes on the road.

From flying taxis to Batman-style surveillance motorcycles, Dubai's GITEX expo this week showcased innovations that were symbols of the city-state's ambitions to be a metropolis of the future. Known for its futuristic skyline and artificial islands, Gulf emirate Dubai has carved out a place alongside cities like Singapore as a hub for innovative ideas. At this year's 37th Gulf Information Technology Exhibition (GITEX), which runs until Thursday, city authorities were keen to show off they remain on the cutting edge. The undisputed star of the expo, which has more than 4,000 companies from 71 countries participating, was Dubai's flying taxi project. It will fly at a height of 120 metres (130 yards), meaning it will be'out of the way of commercial flights' The undisputed star of the expo, which has more than 4,000 companies from 71 countries participating, was Dubai's flying taxi project developed by German drone firm Volocopter.