VTC 2025 Spring Conference’s Shorts

Evaluating Redundancy Mitigation in Vulnerable Road User Awareness Messages for Bicycles

Vehicle-to-X (V2X) communication has become crucial for enhancing road safety, especially for Vulnerable Road Users (VRUs) such as pedestrians and cyclists. However, the increasing number of devices communicating on the same channels will lead to significant channel load. To address this issue this study evaluates the effectiveness of Redundancy Mitigation (RM) for VRU Awareness Messages (VAMs), focusing specifically on cyclists. The objective of RM is to minimize the transmission of redundant information. We conducted a simulation study using a urban scenario with a high bicycle density based on traffic data from Hannover, Germany. This study assessed the impact of RM on channel load, measured by Channel Busy Ratio (CBR), and safety, measured by VRU Perception Rate (VPR) in simulation. To evaluate the accuracy and reliability of the RM mechanisms, we analyzed the actual differences in position, speed, and heading between the ego VRU and the VRU, which was assumed to be redundant. Our findings indicate that while RM can reduce channel congestion, it also leads to a decrease in the perception rate of VRUs. The analysis of actual differences revealed that the RM mechanism standardized by ETSI often uses outdated information, leading to significant discrepancies in position, speed, and heading, which could result in dangerous situations. To address these limitations, we propose an adapted RM mechanism that improves the balance between reducing channel load and maintaining VRU awareness. The adapted approach shows a significant reduction in maximum CBR and a less significant decrease in VPR compared to the standardized RM. Moreover, it demonstrates better performance in the actual differences in position, speed, and heading, thereby enhancing overall safety. Our results highlight the need for further research to optimize RM techniques and ensure they effectively enhance V2X communication without compromising the safety of VRUs.Evaluating Redundancy Mitigation in Vulnerable Road User Awareness Messages for BicyclesNico Ostendorf, Keno Garlichs, Robert Bosch GmbH; Lars Wolf, Technische Universität Braunschweig

DRL-Based Terminal Collaboration in Vehicular Edge Computing

With the increase of users in the Internet of Vehicles (IoV), various heterogeneous user demands are also increasing. The current contradiction in the development of Vehicle Edge Computing (VEC) is how to satisfy all kinds of heterogeneous task requirements in the dynamically changing channel environment. This paper proposes an efficient collaborative scheme for demand-aware terminals, based on spectrum-sharing techniques and Deep Reinforcement Learning (DRL) algorithms, to dynamically satisfy the demands of heterogeneous tasks. Specifically, the delay and energy consumption of two types of tasks are modeled and a multi-objective optimization problem is constructed. Thereafter, we propose a heuristic algorithm to determine the suboptimal solution for optimization variables. Furthermore, we use the DRL algorithm to realize the purpose of dynamically allocating resources according to the channel state. Finally, the scheme’s effectiveness is verified through extensive simulation experiments.DRL-Based Terminal Collaboration in Vehicular Edge Computingwu, sijun; Liang Yang, Hunan University

Deep-Reinforcement-Learning-based Scheduler for Time-Aware Shaper in In-Vehicle Networks

As vehicles develop into software-defined platforms with powerful automated driving capabilities and driver support systems, their in-vehicle networks become significantly more complicated. A key technique for ensuring deterministic, low-latency connectivity for crucial data traffic in such settings is Time-Sensitive Networking (TSN), and specifically the Time-Aware Shaper (TAS). However, current TAS scheduling techniques have difficulty adjusting schedules to dynamically shifting traffic patterns and changing operating conditions. This paper presents an adaptive scheduler using Deep Reinforcement Learning (DRL), which aims to meet strict deadlines, reducing latency and providing near-ideal resource usage. Experimental results for different vehicle scenarios show that our DRL-based scheduler performs better in terms of success rate, low latency, and overall network performance than state-of-the-art heuristic algorithms such as earliest deadline first (EDF) scheduling.Deep-Reinforcement-Learning-based Scheduler for Time-Aware Shaper in In-Vehicle NetworksMohammadparsa Karimi, Majid Nabi, Andrew Nelson, Eindhoven University of technology; Kees Goossens, Twan Basten, Eindhoven University of Technology

Application-Level E2E Demonstration of Reducing Uplink Jitter due to Radio Transmission to...

We experimentally demonstrate real-time end-to-end (E2E) uplink video streaming with our proposed jitter reduction technique for the use case of remote operation. One challenge is that the uplink video quality for remote operation is degraded by jitter due to the radio transmission scheme, such as time division duplexing (TDD), retransmission of lost data units, and data unit concatenation. The proposed technique executes traffic shaping at a router behind a next generation node B (gNB) in a mobile network by using the traffic information obtained from the gNB to calculate the optimal shaping rate. To evaluate the application-level performance of the jitter reduction technique, we carried out E2E uplink video streaming experiments using actual user equipment (UE), gNB, core node, and video server, including actual radio transmission and traffic shaping with the jitter reduction technique at an actual router. Experimental results of evaluating video frame intervals with real-time transport protocol (RTP) header information show that the ratio of video frame intervals that fell within ±5% of the ideal video frame interval value improved from 8.8% without shaping to 20.8% with shaping. These results experimentally demonstrate that jitter due to radio transmission actually degrades video quality in terms of video frame intervals and that the proposed jitter reduction technique effectively reduces it.Application-Level E2E Demonstration of Reducing Uplink Jitter due to Radio Transmission toward Real-Time and Stable Remote OperationKenji Miyamoto, Takumi Harada, Hirotaka Ujikawa, Tatsuya Shimada, Tomoaki Yoshida, NTT Corporation

ODU: Open DSRC Unit toward Effective Dissemination of V2X Applications

In the context of Intelligent Transportation Systems, a Vehicular Ad-Hoc Network provides communication among nearby vehicles and roadside infrastructure. Commercial devices enabling this type of communication via the IEEE 802.11p standard are expensive and proprietary, limiting research and independent field testing. Furthermore, as the industry is their target client, they integrate features like metal waterproof enclosures or mounting brackets, which often result in relatively cumbersome devices unable to accommodate use by vulnerable road users with limited space and weight-bearing capabilities. Aiming to address these concerns, this paper presents a low-cost and open-source device assembled with off-the-shelf components, which we named Open DSRC Unit or ODU in short. The design minimizes the steps required to implement the unit while maintaining a degree of flexibility for potential extension with future upgrades. As a result, the device can act as both a RoadSide Unit and an On-Board Unit, leaving open the possibility of implementing custom messages and acting with custom roles.ODU: Open DSRC Unit toward Effective Dissemination of V2X ApplicationsSalvatore Iandolo, Carlo Augusto Grazia, Martin Klapez, Maurizio Casoni, University of Modena and Reggio Emilia

How the Fusion of Onboard Sensors and V2X Data can improve (or not) the Cooperative Percep...

Automated vehicles rely on onboard sensors to perceive their surroundings and navigate autonomously. However, sensor performance may degrade under adverse weather conditions or when line-of-sight is obstructed. Cooperative perception (or collective perception) is expected to mitigate these limitations by enabling Connected and Automated Vehicles (CAVs) to share sensor data and collaboratively enhance situational awareness. Several studies have analyzed the potential of cooperative perception, yet the fusion of V2X data with information from onboard sensors has received limited focus. V2X data may contain errors that affect the quality of the fused data, and hence the effectiveness of cooperative perception. This study analyzes the impact of sensing measurement errors, V2X packet losses, and GNSS inaccuracies on the effectiveness of cooperative perception. The results highlight the potential of cooperative perception to enhance perception levels and range compared to using onboard sensors alone. However, they also identify key challenges related to the generation of ghost vehicles during the fusion process, which must be addressed to prevent V2X data from introducing additional errors when fused with onboard sensor data.How the Fusion of Onboard Sensors and V2X Data can improve (or not) the Cooperative Perception of Connected Automated VehiclesAmir Mohammadisarab, Miguel Sepulcre, Universidad Miguel Hernandez de Elche (UMH); Luca Lusvarghi, Universidad Miguel Hernandez de Elche; Javier Gozálvez, Universidad Miguel Hernandez de Elche (UMH)

Enhanced Safety Messages (ESM): A Practical Alternative to V2X Basic Safety Messages

A very important use case of V2X communications is the enhancement of roadway safety by utilizing the transmission of road information among vehicles. The SAE Basic Safety Message (BSM) is the most common standard used to transmit road event information and their locations based on global latitudinal and longitudinal coordinates of transmitting vehicles. In practice, however, global coordinate estimations are inherently limited by the accuracy of Global Navigation Satellite Systems (GNSS) such as GPS. GNSS signals can also be unavailable in urban canyons and tunnels, be spoofed to force incorrect localization, or be restricted to low accuracy due to the sparsity of available ground-based corrections (such as RTK base stations) in rural and remote areas. In this paper, we introduce Enhanced Safety Messages (ESMs), a backward-compatible BSM replacement that adopts the well-established foundations of information redundancy in safety-critical systems to avoid catastrophic failure by providing both absolute and relative coordinate frames to robustly describe vehicle locations. This position information redundancy in two different coordinate systems, one from external sources and another from local sensing, effectively addresses the BSM drawbacks of relying entirely on GNSS signals. Specifically, ESM includes LaneContext and MapContext to address two of the most common driving environments of open spaces and urban roadways. Location communication over ESM is enhanced by additionally specifying the connected vehicle’s driving lane, its offset from the center of the lane, and its longitudinal position along the road segment. Our experimental evaluation confirms that ESM’s inclusion of relative positioning rectifies the core BSM weaknesses in relying only on global GNSS coordinates.Enhanced Safety Messages (ESM): A Practical Alternative to V2X Basic Safety MessagesGregory Su, Ragunathan "Raj" Rajkumar, Carnegie Mellon University

Video Quality Control Method Based on Mobile Network Quality Measurements and Predictions ...

Autonomous driving on public roads requires remote monitoring systems for safety. To realize these systems, researchers in academia and industry have focused on controlling network quality metrics, such as bandwidth or latency, and application quality metrics, such as bitrate and framerate. Though conventional application control techniques are based on measured network quality metrics, this causes application deterioration due to late control when the network quality metrics change rapidly. Therefore, recent research has shown the effectiveness of the application control methods based on predicted network quality metrics. However, these methods have limited prediction accuracy, and the predictions might lead to wrong values and critical system problems such as video stalls. To address this issue, we propose a bitrate control method combining both measured and predicted network quality metrics simultaneously to use them complementarily. Finally, in a field experiment, we show our method achieves 28% fewer video stalls and 10% shorter total video stall time than prior methods. This method should contribute to providing reliable remote monitoring services for mobile devices such as autonomous vehicles, machines, and drones.Video Quality Control Method Based on Mobile Network Quality Measurements and Predictions for Reliable Remote Monitoring in Autonomous DrivingMasaki Okada, Nobuhiro Azuma, Takehiro Fujinaga, Takuya Tojo, NTT Network Service Systems Laboratories

Spatial-Temporal Motion Prediction in Cooperative Autonomous Driving System

Cooperative autonomous driving (AD) systems have increasingly become key elements of future intelligent transportation systems owing to the provisioning of dependable, safe, and effective urban mobility operations. In particular, the utilization of motion prediction can contribute to achieving a high-performance cooperative AD planning strategy of the vehicle platoon system. However, realizing accurate spatial-temporal motion prediction is a challenge since most existing work unilaterally considers the spatial or temporal feature in predicting vehicle motion trajectories. To address the problem, we design a novel spatial-temporal Transformer (ST-Transformer) motion prediction model to predict vehicle motion trajectories with high-fidelity simulator. In particular, we integrate both the convolutional and transformer-based networks to capture the spatial-temporal feature of vehicle states. Case studies demonstrate the superiority of the proposed model in predicting autonomous vehicle (AV) trajectories over the existing baseline models, which can greatly support AV motion planning tasks.Spatial-Temporal Motion Prediction in Cooperative Autonomous Driving SystemShiyao Zhang, Great Bay University; Shuyu Zhang, The Hong Kong Polytechnic University; Song Wang, Chongqing Jiaotong University; Shuangyang Li, TU Berlin

Multipath Transmission System using Multi-layer Bandwidth Prediction for Autonomous Drivin...

Autonomous driving technologies have attracted much attention to realize smart mobility societies. Autonomous driving strictly requires uninterrupted mobile communications for remote monitoring in order to ensure safe operation. This requirement is hard to achieve with particular technology such as multipath transmission. In this paper, we propose proactive and reactive multipath transmission mechanisms with multi-layer bandwidth prediction to find degradation of communication quality and avoid it completely. We implemented the proposed mechanisms into the Cooperative Infrastructure Platform and conducted a field evaluation by driving a vehicle on public roads. Our evaluation results show that a 74 - 100% recall rate can be achieved in bandwidth prediction. The Cooperative Infrastructure Platform provides stable communication by accurately controlling multiple mobile networks on the basis of the bandwidth prediction, resulting in only total 3 seconds of video stall for 1 hour of driving.Multipath Transmission System using Multi-layer Bandwidth Prediction for Autonomous DrivingTakuya Tojo, Kotaro Ono, Nobuhiro Azuma, Takehiro Fujinaga, Taichi Kawano, NTT Network Service Systems Laboratories; Mitsuki Nakamura, Nippon Telegraph and Telephone Corporation; Motoharu Sasaki, NTT Corporation; Kenichi Kawamura, NTT Access Network Service Systems Laboratories; Takeshi Kuwahara, NTT Network Service Systems Laboratories