Large trucks substantially contribute to work zone-related crashes, primarily due to their large size and blind spots. When approaching a work zone, large trucks often need to merge into an adjacent lane because of lane closures caused by construction activities. This study aims to enhance the safety of large truck merging maneuvers in work zones by evaluating the risk associated with merging conflicts and establishing a decision-making strategy for merging based on this risk assessment. To predict the risk of large trucks merging into a mixed traffic stream within a work zone, a Long Short-Term Memory (LSTM) neural network is employed. For a large truck intending to merge, it is critical that the immediate downstream vehicle in the target lane maintains a minimum safe gap to facilitate a safe merging process. Once a conflict-free merging opportunity is predicted, large trucks are instructed to merge in response to the lane closure. Our LSTM-based conflict prediction method is compared against baseline approaches, which include probabilistic risk-based merging, 50th percentile gap-based merging, and 85th percentile gap-based merging strategies. The results demonstrate that our method yields a lower conflict risk, as indicated by reduced Time Exposed Time-to-Collision (TET) and Time Integrated Time-to-Collision (TIT) values relative to the baseline models. Furthermore, the findings indicate that large trucks that use our method can perform early merging while still in motion, as opposed to coming to a complete stop at the end of the current lane prior to closure, which is commonly observed with the baseline approaches.

Real-world traffic involves diverse road users, ranging from pedestrians to heavy trucks, necessitating effective road user classification for various applications within Intelligent Transport Systems (ITS). Traditional approaches often rely on intrusive and/or expensive external hardware sensors. These systems typically have limited spatial coverage. In response to these limitations, this work aims to investigate an unintrusive and cost-effective alternative for road user classification by using high-frequency (1-2 Hz) positional sequences. A cutting-edge solution could involve leveraging positioning data from 5G networks. However, this feature is currently only proposed in the 3GPP standard and has not yet been implemented for outdoor applications by 5G equipment vendors. Therefore, our approach relies on positional data, that is recorded under real-world conditions using Global Navigation Satellite Systems (GNSS) and processed on distributed edge devices. As a start-ing point, four types of road users are distinguished: pedestri-ans, cyclists, motorcycles, and passenger cars. While earlier approaches used classical statistical methods, we propose Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) as the preferred classification method, as they repre-sent state-of-the-art in processing sequential data. An RNN architecture for road user classification, based on selected motion characteristics derived from raw positional sequences is presented and the influence of sequence length on classifica-tion quality is examined. The results of the work show that RNNs are capable of efficiently classifying road users on dis-tributed devices, and can particularly differentiate between types of motorized vehicles, based on two- to four-minute se-quences.

Urban intersections, filled with a diverse mix of vehicles from small cars to large semi-trailers, present a persistent challenge for traffic control and management. This reality drives our investigation into how robot vehicles (RVs) can transform such heterogeneous traffic flow, particularly at unsignalized intersections where traditional control methods often falter during power failures and emergencies. Using reinforcement learning (RL) and real-world traffic data, we study heterogeneous mixed traffic across complex intersections under gradual automation by varying RV penetration from 10% to 90%. The results are compelling: average waiting times decrease by up to 86% and 91% compared to signalized and unsignalized intersections, respectively. Additionally, we uncover a "rarity advantage," where less frequent vehicles, such as trucks, benefit the most from RV coordination (by up to 87%). RVs' presence also leads to lower CO2 emissions and fuel consumption compared to managing traffic via traffic lights. Moreover, space headways decrease across all vehicle types as RV rate increases, indicating better road space utilization.

Though often overlooked, cars serve as a rich data source. Millions of transportation vehicles whizz past us on a regular basis, each of which generate swaths of useful information that automakers are now figuring out how to monetize. Some of the biggest passenger car automakers have more than 10 million vehicles' worth of data sitting in their data repositories. Failure to tap into these vast data stores amounts to lost value-added for customers, lost safety opportunities and lost revenue and business intelligence. According to a McKinsey Report, "The overall revenue pool from car data monetization at a global scale might add up to USD 450 - 750 billion by 2030." In addition, according to a market analysis report on the Automotive Cyber Security Market, "The global automotive cyber security market size was valued at USD 1.44 billion in 2018 and is expected to grow at a compound annual growth rate (CAGR) of 21.4% from 2019 to 2025."

In the U.S. alone, revenues from the trucking industry rose to $796.7 billion in 2018, up from $700.1 billion the previous year, according to the American Trucking Associations. Trucks moved more than 70% of the country's freight. A major factor for businesses in choosing self-driving trucks is greater fuel efficiency, which cuts fuel costs by at least 15%, according to Plus.ai. "There's no question that autonomous trucks will be ready before autonomous cars," Plus.ai COO and co-founder Shawn Kerrigan said in a statement to CNBC.

General Motors Co said on Monday it will cut production of slow-selling models and slash its North American workforce in the face of a stagnant market for traditional gas-powered sedans, shifting more investment to electric and autonomous vehicles. The announcement is the biggest restructuring in North America for the US's largest carmaker since its bankruptcy a decade ago. Its shares rallied 7.6 percent to $38.66. GM plans to halt production next year at three assembly plants - Lordstown, Ohio, Hamtramck, Michigan, and Oshawa, Ontario. The company also plans to stop building several models now assembled at those plants, including the Chevrolet Cruze, the Cadillac CT6 and the Buick LaCrosse.

Waymo has admitted in a blog post that one of its test vehicles hit a motorcycle in Mountain View. The company has defended its technology in the post, though, clarifying that the event was caused by human error. Apparently, the test driver took control of the vehicle after seeing a passenger car to the left moving into their lane. Waymo says they moved the car to the right lane without noticing that a motorcycle had moved from behind to pass the test vehicle. The test car sustained minor damage, but the collision was unfortunately serious enough to send (PDF) the motorcyclist to the hospital. According to the Alphabet-owned company, it was exactly the type of incident self-driving vehicles could prevent.

In America, the age of autonomous shuttles began with a crunch. On Wednesday, the multinational transportation company Keolis, French manufacturer Navya, and AAA launched the small driverless vehicle in Las Vegas. The electric vehicle had an attendant on board, to keep the peace, and carried eight people in a half-mile loop around the Fremont Street Entertainment District. According to representatives from Keolis and AAA, as well as a first-person account published in Digital Trends, the shuttle encountered a semi-truck backing out of an alleyway and stopped. It couldn't back up, because there was a vehicle directly behind it.

The use of Artificial Neural Networks (ANNs) towards developing Artificial Intelligence (AI) has undergone a renaissance in the past decade. Out of the many emergent techniques for training ANNs that are collectively referred to as'Deep Learning', Deep Reinforcement Learning (DRL) is proving to be a particularly general and powerful method, with applications ranging from video games [1] to autonomous driving [2]. While most applications of reinforcement learning have traditionally used reinforcement signals derived from performance measures that are explicit to the task - e.g. the score in a game or grammatical errors in a translation, when considering AI systems that are required to have a significant interaction with humans - e.g. the autonomous vehicle - it is critical to consider how the human's preference for objects, events, or actions can be incorporated into the behavioral reinforcement for the AI, particularly in ways that are minimally obtrusive [3], [4]. Such behavioral adaptations occur naturally during social interactions and form the bedrock of social mechanisms that build trust and rapport between strangers [5], [6]. In this paper, we present a novel approach that uses decoded human neurophysiological and ocular time-series data as an implicit reinforcement signal for an AI agent that is driving a virtual automobile.

Artificial intelligence is set to be the stepping stone between driver assistance systems and truly autonomous vehicles. You'd be forgiven for thinking that fully autonomous cars were just around the corner. In some respects, of course, they are. Partial automation – along the lines of Tesla's much-publicised Autopilot – is set to become commonplace on premium cars over the next few years. Even when it comes to higher levels of autonomy, much of the required hardware is already available.