Toyota Motor North America, InfoTech Labs

The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. Also called “phantom jams” or “stop-and-go waves,” these instabilities are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system, referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment, the MegaVanderTest (MVT), leveraged a heterogeneous fleet of 100 longitudinally controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this article. The MegaController is a hierarchical control architecture that consists of two main layers. The upper layer is called the Speed Planner and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock onboard sensors. The Speed Planner ingests live data feeds provided by third parties as well as data from our own control vehicles and uses both to perform the speed assignment. The architecture of the Speed Planner allows for the modular use of standard control techniques, such as optimal control, model predictive control (MPC), kernel methods, and others. The architecture of the local controller allows for the flexible implementation of local controllers. Corresponding techniques include deep reinforcement learning (RL), MPC, and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers or only some. Likewise, control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars to electronic selection of adaptive cruise control (ACC) setpoints in others. The proposed architecture technically allows for the combination of all possible settings proposed previously, that is {Speed Planner algorithms} × {local Vehicle Controller algorithms} × {full or partial sensing} × {torque or speed control}. Most configurations were tested throughout the ramp up to the MegaVandertest (MVT).

Automated Vehicle Marshalling (AVM) is an innovative technology poised to transform the automotive industry by enabling automated vehicles to be wirelessly controlled within geofenced areas while ensuring guaranteed Functional Safety (FuSa). Significant investments from major automakers and suppliers are driving the advancement of this SAE Level 4 driverless technology. Standardization is a crucial prerequisite for the widespread deployment of AVM, requiring collaboration among academia, international standardization bodies (e.g., ISO, ETSI, SAE), and industry consortia such as VDA and 5GAA. This paper outlines the current standardization efforts and deployment status of AVM, elaborates on core vehicle motion control mechanisms, FuSa principles, communication interfaces, message formats, and spectrum requirements. Through this comprehensive examination, the paper aims to address how AVM can be seamlessly integrated into future Intelligent Transportation System (ITS) ecosystems. As the automotive industry progresses toward greater automation and connectivity, AVM represents a major advancement in automated vehicle maneuvering and control for manufacturing plants, logistics depots, parking facilities, and charging stations.

Perceiving the environment is one of the most fundamental keys to enabling Cooperative Driving Automation, which is regarded as the revolutionary solution to addressing the safety, mobility, and sustainability issues of contemporary transportation systems. Although an unprecedented evolution is now happening in the area of computer vision for object perception, state-of-the-art perception methods are still struggling with sophisticated real-world traffic environments due to the inevitable physical occlusion and limited receptive field of single-vehicle systems. Based on multiple spatially separated perception nodes, Cooperative Perception (CP) is born to unlock the bottleneck of perception for driving automation. In this paper, we comprehensively review and analyze the research progress on CP, and we propose a unified CP framework. The architectures and taxonomy of CP systems based on different types of sensors are reviewed to show a high-level description of the workflow and different structures for CP systems. The node structure, sensing modality, and fusion schemes are reviewed and analyzed with detailed explanations for CP. A Hierarchical Cooperative Perception (HCP) framework is proposed, followed by a review of existing open-source tools that support CP development. The discussion highlights the current opportunities, open challenges, and anticipated future trends.

Objective: The objective of this study is to use parametric human modeling and machine learning methods to identify representative occupants that can account for injury variations among a more diverse population with a limited simulation budget. Method: A maximal projection method was used to sample 100 occupants, considering the variations in stature, weight, and sitting height. An automated mesh morphing method was used to morph the THUMS v4.1 midsize male model into the target geometries. US-NCAP frontal crash simulations were conducted with morphed human models and validated vehicle/restraint models. Surrogate models based on the Gaussian Process (GP) method were trained to find inducing points (IP), here defined as a small number of representative occupants whose outcomes could be used to accurately estimate the variations in the injury risks and patterns throughout the population. Statistical analysis was conducted to validate the IPs' coverage of total variation by illustrating the IP distribution. Restraint optimization was performed at IPs to yield an enhanced restraint system. The method was validated through comparisons among the predicted injury risks under the optimal and baseline designs. Results: Only 20 IPs were needed to sufficient to properly represent the variations in the injury risks and patterns in the whole population with acceptable accuracy. Compared to the surrogate model built from 100 crash simulations, the IP-based surrogate models incurred only 0.4% and 1.8% errors in head injury risks for males and females, respectively. Regarding the injury risks on the chest and lower extremities, the IP-based surrogate models resulted in less than 0.1% and 0.5% errors for males and females, respectively. The FE simulations indicated that the optimal restraint system design reduced the injury risk by relatively 16% and 13% for male and female respectively when delta-V = 25 (mph), and 47% and 27% for male and female when delta-V = 35 (mph). Significance of results: The study proposed a method to generate more accurate injury risk predictions for a more diverse population under a limited simulation budget. Simulations using IPs may enable restraint system optimization to be conducted more efficiently while reducing injury risks across a more diverse population.

Teleoperated driving (ToD) enables the remote driving or control of vehicles. For this purpose, vehicles must transmit video feeds to the ToD control center so that the remote operator is fully aware of the driving conditions and can safely control the vehicle. 5G (and beyond) networks are fundamental for the deployment of ToD as they can provide the low latency, reliable and broadband connection necessary to connect the vehicle and ToD control center. However, it is unclear whether common 5G network architectures and configurations are well-suited to support the simultaneous teleoperation of multiple vehicles with demanding uplink bandwidth, as current networks are mainly configured to support mobile broadband services. This paper demonstrates that MEC or edge-based 5G networks are better suited to support and scale the ToD service than centralized networks, and quantifies the bandwidth required to simultaneously teleoperate multiple vehicles under various 5G network architectures and configurations, including different duplexing modes and TDD frame structures. Finally, the study shows that the configuration of the control channels can help mitigate the impact that the processing time of the video feeds has on the capacity to support and scale the ToD service.

Connected Automated Vehicles (CAVs) will use multiple V2X services to support connected and automated driving functions. The bandwidth required to support such services will augment as CAVs are gradually deployed. It is therefore important to accurately estimate the spectrum requirements to anticipate possible scalability challenges ahead. Current estimations consider a simplified modeling of the transmitter as well as context factors such as the number of vehicles in the communication range. Moreover, they do not accurately model if the Quality of Service (QoS) of the considered V2X services is satisfied or not. This study progresses the state of the art with a novel analytical model that quantifies the bandwidth required to support multiple V2X services. The model considers the impact of the vehicular context, the transmission parameters and the communication requirements to take into account the QoS at the receiver. This is important since adapting the transmission parameters can reduce the channel load but also impacts the probability to correctly receive each packet and therefore the bandwidth required to guarantee a target QoS at the receiver. The proposed model can be adapted to different wireless technologies and messages, but is applied in this study to quantify the bandwidth required by LTE-V2X to support the transmission of CAMs, CPMs and MCMs. The study demonstrates the scalability challenges ahead to support multiple V2X services.

Vehicular Micro Cloud (VMC) is a group of connected vehicles where vehicles collaborate on a task over the vehicular network. A potential use case of VMC is that micro cloud members transfer data to each other via Vehicle-to-Vehicle (V2V) links, and the data is collaboratively uploaded to remote server (e.g., data center) when the connected vehicles are connected to a Wi-Fi network. In this paper, we focus on this use case and propose collaborative upload by VMC. We demonstrated the feasibility of the proposed method through the large-scale urban simulation (Los Angeles downtown traffic model). Our simulation results showed that the proposed method can reduce the upload data of traditional cellular network-based data upload by 50%. Index Terms-Data uploading/gathering, Vehicular Micro Cloud (VMC), Vehicle-to-Vehicle (V2V) communication

Integrating Connected Autonomous Vehicles (CAVs) on the road presents numerous opportunities for safety and mobility enhancement. One of the main features that CAVs introduce is gathering data that conventional vehicles cannot currently access. Information such as traffic jams or hazards downstream can impact a driver’s lane choice, thereby improving or potentially worsening their experience. This information can be collected by CAVs and disseminated through a cloud system to vehicles upstream. Hence, significantly enhancing the lane-level decision making process. In this work, we propose a dynamic lane-level forward graph that considers different contents on the road that can be static, semi-static or dynamic. Our proposed approach effectively integrates map data as well as downstream data collected by CAVs to present a policy to optimally select the best lane given this information. To evaluate the proposed approach, simulations were carried out using the microsimulation tool, Aimsun, across different CAV market penetration rates. Results show that the proposed approach can reduce travel time by 20%, while also improving traffic safety.

Intent sharing is a class of cooperation enabled by vehicle-to-everything (V2X) communication, which allows for information exchange between road users about their intended future behaviors. In this paper, we propose a generalized representation of vehicles’ motion intent from a dynamical systems viewpoint. Based on this, we extend the framework of conflict analysis such that intent information can be interpreted in real time to assist the decision-making of intent-receiving vehicles and ensure conflict-free maneuvers. We create intent messages using commercially available V2X radios, and demonstrate experimentally the benefits of sharing intent in cooperative maneuvering. Experiments are performed on a test track where intent-based on-board decision assistance is provided to human drivers in merge scenarios. The experimental results reveal significant benefits of intent sharing in enhancing vehicle safety and time efficiency. Furthermore, we test intent messages on public roads and evaluate the performance in terms of packet delivery ratio. The data collected on public highways are fed into numerical simulations to investigate the effects of intent transmission conditions on conflict resolution.