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# Delay Performance in ITS-G5-Based Vehicular Ad Hoc Networks (VANETs)

Paul Mühlethaler

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## To cite this version:

Paul Mühlethaler. Delay Performance in ITS-G5-Based Vehicular Ad Hoc Networks (VANETs). AIO Inria Paris. 2025. hal-05474840

HAL Id: hal-05474840

https://hal.science/hal-05474840v1

Submitted on 23 Jan 2026

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# Delay Performance in ITS-G5-Based Vehicular Ad Hoc Networks (VANETs)

Paul Muhlethaler - AIO Inria Paris

6 Juin 2025

## Abstract

Vehicular Ad Hoc Networks (VANETs) are fundamental in advancing Intelligent Transportation Systems (ITS) by enabling real-time communication among vehicles and infrastructure. The European ITS-G5 standard, based on IEEE 802.11p, is a prominent technology facilitating Vehicle-to-Everything (V2X) communications. This article analyzes the delay performance of ITS-G5-based VANETs, examining factors influencing latency and evaluating strategies to mitigate delays. Through a comprehensive review of existing literature, we highlight the challenges and propose solutions to enhance the efficiency of ITS-G5 communications. We show that MAC-layer contention and network density remain the dominant sources of delay in ITS-G5, and that hybrid and adaptive approaches are key to ensuring scalable low-latency performance.

## 1 Introduction

The evolution of transportation systems towards increased automation and connectivity has underscored the importance of reliable and low-latency communication networks [1]. VANETs, as a subset of mobile ad hoc networks, facilitate direct communication among vehicles (V2V) and between vehicles and infrastructure (V2I), enhancing road safety and traffic efficiency [4]. The ITS-G5 standard, developed by ETSI, leverages the IEEE 802.11p protocol to support these communications in the 5.9 GHz band [2].

ITS-G5 is specifically designed to meet the real-time requirements of Cooperative Intelligent Transport Systems (C-ITS). It enables the periodic
broadcast of Cooperative Awareness Messages (CAMs), which inform surrounding vehicles of position, speed, and heading, as well as Decentralized Environmental Notification Messages (DENMs), which provide alerts about sudden or unexpected events such as accidents, obstacles, or dangerous road conditions. These messages are critical for applications such as collision avoidance, emergency braking, and cooperative adaptive cruise control.

## 2 ITS-G5 Communication Framework

ITS-G5 operates in the 5.875-5.905 GHz frequency band, employing Orthogonal Frequency Division Multiplexing (OFDM) at the physical layer and Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) at the Medium Access Control (MAC) layer. This configuration is inherited from the IEEE 802.11p amendment, which was specifically developed to support vehicular environments characterized by high mobility and rapidly changing topologies. OFDM allows for robust transmission over multipath fading channels, which are common in urban canyons and highway scenarios, while CSMA/CA facilitates a decentralized, contention-based access mechanism suitable for ad hoc networks without centralized scheduling.

Unlike cellular-based systems, ITS-G5 does not rely on fixed infrastructure such as base stations. Instead, it enables vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication in a peer-to-peer fashion, ensuring that safety-critical messages can be disseminated even in areas with poor or no cellular coverage. This is particularly important for applications that demand ultra-low latency, such as collision avoidance or emergency braking warnings, where decisions must be made within milliseconds.

The ITS-G5 communication stack includes the European standard ETSI ITS protocols on top of the 802.11p PHY/MAC. Among the core functionalities of ITS-G5 are the periodic broadcast of Cooperative Awareness Messages (CAMs) and the event-driven transmission of Decentralized Environmental Notification Messages (DENMs) [10, 11]. CAMs are typically generated up to ten times per second and contain real-time information about a vehicle's position, speed, heading, and status (e.g., brake lights on, emergency vehicle mode). These messages help build a situational awareness map for surrounding vehicles.

DENMs, on the other hand, are triggered by specific events such as the detection of obstacles, accidents, roadworks, or adverse weather conditions.
They are distributed dynamically across the network using geographic routing, allowing vehicles outside the line-of-sight to be warned in advance. Together, CAMs and DENMs form the basis for a range of Cooperative Intelligent Transport System (C-ITS) services, improving road safety, traffic efficiency, and environmental sustainability.

# 3 Factors Influencing Delay

## 3.1 MAC Layer Challenges

The CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) mechanism, inherited from IEEE 802.11p, is used in ITS-G5 to coordinate medium access among vehicles in a decentralized fashion. While CSMA/CA is simple and effective in lightly loaded networks, it suffers significant limitations under high vehicular density and dynamic topology changes [8, 19]. In such conditions, multiple nodes often attempt to access the channel simultaneously, leading to frequent collisions, backoffs, and retransmissions, which introduce variable delays and degrade overall throughput [9].

A fundamental challenge arises from the broadcast nature of safety-critical messages such as CAMs and DENMs. Since these packets are not acknowledged at the MAC layer, retransmissions cannot be initiated upon loss. As a result, delay and delivery reliability depend solely on initial channel access success. During periods of high message generation—such as dense urban intersections or traffic jams—the probability of collisions increases sharply. As a result, nodes may experience excessive waiting times before accessing the medium. This problem is exacerbated by the hidden terminal effect, especially when vehicles are not within mutual sensing range.

Additionally, CSMA/CA lacks any prioritization mechanism natively suited to differentiate message types based on urgency or relevance. Although ETSI has defined channel access categories with different contention window sizes and inter-frame spacing parameters (similar to IEEE 802.11e's Enhanced Distributed Channel Access), these adaptations remain insufficient under extreme traffic loads. This limitation makes it difficult to guarantee bounded delay for latency-sensitive applications such as emergency electronic brake light (EEBL) alerts or intersection collision warnings.

The random nature of backoff timers, combined with variable network density, results in high delay jitter—an undesirable trait for time-synchronized
applications. Furthermore, as the number of active nodes grows, the effec-
tive throughput per node decreases due to increased contention, leading to a
trade-off between scalability and latency. In highway scenarios, where vehi-
cle density can vary rapidly, the MAC protocol must continuously adapt to
avoid both underutilization and overload.

Various studies have proposed enhancements to the basic CSMA/CA mechanism. One widely adopted approach is Decentralized Congestion Control (DCC), which dynamically adjusts transmission parameters (e.g., message rate, power level, contention window) based on observed channel load. Another method involves adaptive beaconing strategies that modulate CAM frequency depending on vehicle dynamics or environmental context. While these techniques offer improvements, they also introduce new complexities and trade-offs between delay, fairness, and message relevance.

Ultimately, the inherent limitations of CSMA/CA in highly dynamic and dense VANET environments underscore the need for more robust and context-aware MAC layer solutions. Hybrid approaches that combine contention-based access with scheduled or prioritized schemes—potentially integrated with cellular-V2X mechanisms—represent a promising direction for future development.

## 3.2 Network Density and Mobility

High vehicle density and mobility patterns impact communication delays through rapid topology changes and frequent route recalculations [5,6]. This dynamic environment makes maintaining low delay difficult.

In densely populated areas—such as urban intersections, traffic jams, or highway congestion—hundreds of vehicles may attempt to communicate simultaneously within a small geographic region. This situation leads to increased channel contention and higher collision rates, causing retransmissions and additional backoff delays at the MAC layer. The situation becomes more critical for broadcast messages, such as CAMs and DENMs, which are not acknowledged and therefore not retransmitted in case of loss. As a result, packet delivery delay increases, and the reliability of safety-critical communications may be compromised.

Moreover, network density can vary rapidly depending on time of day, road infrastructure, or local events (e.g., concerts, sports venues). This variability makes it difficult to predict network conditions and adapt transmission parameters accordingly. High-density conditions are particularly challenging
for ITS-G5, which relies on a contention-based MAC protocol that does not scale linearly with the number of users.

In contrast, in sparse scenarios—such as rural highways or nighttime conditions—connectivity gaps may occur between vehicles. These "communication holes" can lead to frequent disconnections and the need for multi-hop relaying, which introduces additional delays and increases the risk of outdated information propagating through the network.

Vehicle mobility also plays a crucial role in shaping network dynamics. At highway speeds, vehicles enter and leave communication ranges within seconds, leading to frequent neighbor table updates and broken links. This makes routing especially difficult, even when using position-based protocols. High-speed mobility also causes Doppler shifts and fluctuating signal strength, which degrade the stability and quality of wireless links. When combined with dense environments, this mobility can lead to bursty packet losses and inconsistent message reception patterns.

Additionally, relative motion between vehicles affects the temporal relevance of transmitted data. For example, a CAM received after 500 ms may no longer reflect the accurate position or heading of a fast-moving vehicle, rendering it less useful for collision avoidance or trajectory planning.

To address these challenges, VANET protocols must be robust to frequent topological changes, and ideally, adaptive to both density and mobility. Techniques such as adaptive message rate control, geographical forwarding, and opportunistic relaying have been proposed in the literature to mitigate the negative impact of density and mobility on delay. Nonetheless, ensuring consistent low-latency communication in such a highly dynamic environment remains an open research problem.

## 3.3 Environmental Factors

Obstructions and urban environments cause signal attenuation and multipath propagation, affecting delay [3,7]. In real-world scenarios, the propagation of wireless signals is rarely ideal. Physical obstacles such as buildings, bridges, trucks, and road infrastructure significantly degrade signal quality through shadowing, diffraction, and reflection. This leads to increased bit error rates, reduced signal-to-noise ratio (SNR), and intermittent communication outages—all of which can introduce variable delays and impact the reliability of message delivery.
Urban canyons, created by high-rise buildings in dense cities, are par-
ticularly problematic for ITS-G5 systems, as they cause severe multipath
propagation where signals reach the receiver over multiple paths with dif-
ferent delays. This results in inter-symbol interference, which may require
additional processing time or cause packet corruption, leading to retransmis-
sions or delays in communication. In some cases, the wireless channel may be
entirely blocked, forcing vehicles to rely on indirect multi-hop paths, which
inherently increase latency.

Environmental conditions also play a key role in delay performance. For example, heavy rain, snow, or fog can impact radio wave propagation, particularly in the 5.9 GHz band used by ITS-G5. These weather-related degradations reduce communication range and reliability, potentially causing temporary loss of connectivity or forcing a vehicle to switch to fallback communication methods.

In non-line-of-sight (NLOS) scenarios—such as around corners, behind trucks, or underpasses—vehicles may not be able to detect or communicate directly with nearby participants. This leads to communication "gray zones" where reception is unpredictable or inconsistent [7]. In such zones, vehicles may receive incomplete or delayed information, reducing the effectiveness of safety-critical applications like emergency braking alerts or intersection collision warnings.

Moreover, the dynamic nature of the environment—where obstacles can
appear and disappear rapidly (e.g., due to moving buses or changing traffic
patterns)—makes it difficult for the system to maintain consistent channel
quality. The variability of the radio environment requires communication
protocols to be highly adaptive, both in terms of transmission power and
message redundancy.

To mitigate the impact of environmental factors, several approaches have been explored in the literature. These include the use of environment-aware adaptive transmission schemes, multi-antenna systems (e.g., MIMO), and cooperative relaying where vehicles help forward messages on behalf of others that are temporarily disconnected. Some studies also propose integrating ITS-G5 with cellular networks or using roadside units (RSUs) as fixed relays to bridge gaps in coverage and reduce delay in obstructed areas.

Ultimately, accounting for environmental variability is essential when evaluating and optimizing the delay performance of ITS-G5-based vehicular networks, particularly in safety-critical scenarios where even milliseconds can make a significant difference.
# 4 Strategies for Delay Mitigation

## 4.1 Decentralized Congestion Control (DCC)

ETSI's Decentralized Congestion Control (DCC) is a core component of the ITS-G5 architecture, designed to maintain communication performance in dense and highly dynamic vehicular networks. It dynamically adjusts transmission parameters—such as message frequency, transmit power, and data rate—based on local observations of channel activity, in order to prevent congestion and reduce delay [12, 13].

The DCC framework operates in a decentralized and autonomous manner, meaning that each vehicle individually assesses its surrounding channel conditions without relying on centralized coordination. This assessment is typically based on metrics such as the Channel Busy Ratio (CBR), which reflects the percentage of time the communication channel is sensed as occupied. When the CBR exceeds predefined thresholds, the DCC logic transitions between three operational states—Relaxed, Active, and Restrictive—each associated with specific transmission constraints.

In the Relaxed state, vehicles are allowed to transmit at high frequency and power, suitable for low-density or sparse environments. As the CBR increases and congestion risk rises, nodes shift into the Active state, where more conservative settings are applied. Under extreme congestion, the Restrictive state imposes tight limitations on message frequency and power to alleviate channel load.

DCC's design ensures that the most critical messages—particularly CAMs and DENMs—can still be delivered under high-load conditions, albeit with potentially reduced resolution or range. By dynamically tuning the rate of CAM generation, DCC reduces redundant transmissions and limits channel contention. For example, a vehicle cruising steadily on a highway may reduce its CAM frequency, while a braking or turning vehicle may increase its message rate to inform nearby nodes of its behavior change.

Despite its effectiveness, DCC faces several implementation challenges. In particular, the responsiveness of the DCC state machine can vary significantly depending on parameter tuning, which affects how quickly a node reacts to changes in channel conditions. Overly aggressive transitions may lead to unstable behavior, while delayed reactions may result in prolonged congestion periods. Furthermore, since DCC operates in a decentralized manner, vehicles in close proximity may make inconsistent or conflicting decisions based
on slightly different local observations, reducing the overall coherence of the
system.

Another challenge lies in the interaction between DCC and higher-layer
applications. Some applications may require high-frequency updates regard-
less of channel load, leading to potential conflicts between application require-
ments and DCC restrictions. Additionally, DCC currently does not differen-
tiate between message priorities beyond basic access categories, which limits
its ability to guarantee ultra-low latency for certain safety-critical messages.

To improve its performance, ongoing research explores context-aware
DCC schemes, where parameters are adapted not only based on channel
load but also on situational awareness (e.g., traffic density, road topology,
vehicle role). Other proposals include predictive congestion control and the
integration of DCC with machine learning models to better anticipate and
manage traffic bursts.

In summary, DCC plays a vital role in maintaining the scalability and
reliability of ITS-G5 communications by managing access to the wireless
medium. However, further optimization and standardization are needed to
fully support the diverse QoS requirements of modern C-ITS applications.

## 4.2 Adaptive Beaconing

Adaptive beaconing modifies CAM frequency based on context to reduce unnecessary traffic [14]. Instead of sending Cooperative Awareness Messages (CAMs) at a fixed rate—typically 10 Hz as specified in the baseline ITS-G5 profile—adaptive beaconing schemes dynamically adjust the frequency depending on factors such as vehicle mobility, traffic density, and situational relevance. This approach aims to reduce channel congestion, lower the risk of packet collisions, and ultimately decrease communication delay in dense vehicular environments.

The key rationale behind adaptive beaconing is that vehicles do not always need to broadcast at the highest possible frequency. For instance, when a vehicle is cruising steadily at constant speed and direction, its state changes slowly and less frequent CAM updates may be sufficient for neighboring vehicles to track it accurately. Conversely, during dynamic maneuvers such as acceleration, braking, or lane changes, more frequent updates are necessary to ensure timely situational awareness.

Several adaptation strategies have been proposed in the literature. One
common approach is "mobility-based adaptation", where the beacon rate is
increased proportionally with changes in speed or heading. Another method is "context-based adaptation", which considers external factors such as vehicle proximity, intersection presence, or environmental hazards. Some systems also employ "channel-aware adaptation", where the CAM rate is reduced in response to high Channel Busy Ratio (CBR) to avoid further overloading the medium.

In ETSI's ITS-G5 framework, adaptive beaconing is supported through DCC integration, where the DCC state determines the upper limit of allowable CAM frequency. However, finer-grained beaconing logic can be implemented at the application or facilities layer to make CAM emission more intelligent and context-sensitive.

The benefits of adaptive beaconing include not only reduced channel load and improved scalability but also better "delay performance", as fewer redundant transmissions free up bandwidth for urgent messages such as DENMs. Moreover, adaptive strategies help maintain high information freshness (low Age of Information) by focusing transmissions when they are most needed, rather than adhering to a rigid periodic schedule.

Despite its advantages, adaptive beaconing also presents challenges. Aggressive reduction of CAM frequency may lead to temporal gaps in awareness, especially in fast-changing environments. This trade-off must be carefully balanced to ensure that critical state changes are not missed. Additionally, the lack of standardization across manufacturers regarding adaptation policies may lead to inconsistent behavior between vehicles in mixed environments.

Ongoing research explores the integration of machine learning and vehicular sensor fusion to further optimize beaconing policies. These intelligent systems can learn from historical data and driving context to predict when updates are most necessary, offering a promising path toward more efficient and delay-resilient VANET communications.

## 4.3 Hybrid Architectures

Hybrid models combining ITS-G5 and cellular networks—such as LTE-V2X and 5G NR-V2X—offer enhanced performance in terms of communication range, reliability, and latency [12, 15, 16]. While ITS-G5 excels in low-latency, short-range, broadcast communication—particularly suited for safety-critical applications—cellular-based V2X technologies provide superior scalability, wider area coverage, and support for network-assisted services.
In hybrid architectures, both communication technologies operate concurrently. Vehicles may switch between, or simultaneously use, both channels depending on context, availability, and application requirements. For instance, time-sensitive, localized messages such as Cooperative Awareness Messages (CAMs) and Decentralized Environmental Notification Messages (DENMs) can be transmitted over ITS-G5 to benefit from its low latency and direct vehicle-to-vehicle communication. Meanwhile, applications requiring long-range communication, backhaul connectivity, or cloud interaction—such as traffic management, infotainment, or over-the-air updates—can rely on cellular infrastructure.

This division of roles not only offloads traffic from the cellular network but also mitigates the congestion and delay problems inherent in dense ITS-G5-only deployments. In situations where line-of-sight communication is obstructed or ITS-G5 signals are weakened (e.g., tunnels, urban canyons), cellular connectivity can act as a fallback, ensuring continuity of service.

Hybrid systems can also support multi-access edge computing (MEC), where computing resources are deployed at the edge of the network, closer to the vehicle. This enables low-latency data processing and decision-making for use cases like cooperative perception and intersection management. Furthermore, hybrid architectures allow for enhanced Quality of Service (QoS) management by dynamically allocating the best-suited communication interface based on metrics like latency, reliability, and channel load.

However, implementing hybrid models introduces challenges in terms of interoperability, handover management, and consistency of data across interfaces. Ensuring seamless switching between ITS-G5 and cellular V2X requires tight synchronization and harmonization of protocol stacks. Moreover, security and privacy mechanisms must be unified to maintain trust across different communication domains.

Standardization efforts, such as those driven by 3GPP and ETSI, are actively working toward enabling hybrid V2X deployment models. These include defining mechanisms for radio interface selection, service continuity, and cross-technology cooperation. From a deployment perspective, vehicle manufacturers and infrastructure providers are increasingly adopting dual-radio platforms to support hybrid communication, anticipating future requirements for autonomous driving and cooperative ITS services.

In summary, hybrid architectures represent a promising approach to overcoming the limitations of standalone V2X technologies. By combining the strengths of ITS-G5 and cellular networks, they offer a flexible and robust
communication framework capable of supporting both current safety-critical
applications and emerging use cases requiring high data rates, reliability, and
ultra-low latency.

5 Comparative Analysis

Studies comparing ITS-G5 and C-V2X show trade-offs in coverage and la-
tency depending on traffic density and infrastructure support [17,18]. Both
technologies are designed to enable Vehicle-to-Everything (V2X) communica-
tions, but their underlying architectures and operational characteristics differ
significantly, leading to distinct performance profiles across various use cases.
Recent comparative surveys further confirm that no single technology uni-
formly outperforms the other across all deployment scenarios, as performance
strongly depends on network load, propagation conditions, and application
requirements [21].

ITS-G5, based on IEEE 802.11p, enables direct communication between vehicles using a decentralized, ad hoc approach. It provides extremely low latency, typically below 10 milliseconds for one-hop communication<fnref n="1" />, making it highly suitable for time-critical safety applications such as emergency braking alerts and intersection collision warnings. However, its performance can degrade rapidly in highly dense environments due to the limitations of its contention-based MAC protocol (CSMA/CA), which may lead to increased collisions, large backoff delays, and unpredictable delay jitter.

C-V2X, particularly in its LTE-V2X and 5G NR-V2X forms, is a cellular-
based alternative developed by 3GPP. Unlike ITS-G5, it supports both direct
communication (sidelink PC5 interface) and network-based communication
(Uu interface). LTE-V2X in Mode 4 allows vehicles to communicate directly
without relying on cellular infrastructure, similar to ITS-G5, but uses a semi-
persistent and sensing-based scheduling mechanism instead of CSMA/CA.
This approach provides improved scalability and reliability in high-density
scenarios by reducing collision probability and controlling channel contention.
Recent studies on 5G NR-V2X sidelink further highlight enhanced robust-
ness and more predictable latency under heavy traffic loads compared to
contention-based access mechanisms [20].

In contrast, the network-assisted Mode 3 (available in LTE and 5G) en-
ables centralized scheduling through the base station, offering improved coor-

<footnote n="1">¹In low-density line-of-sight (LOS) scenarios.</footnote>
dination, load balancing, and Quality of Service (QoS) guarantees. However, this mode is inherently dependent on infrastructure availability and coverage, which may not always be present in rural, remote, or disaster-affected areas, thereby limiting its applicability for fully infrastructure-independent safety services.

When comparing coverage, C-V2X holds a distinct advantage due to its integration with cellular infrastructure, providing extended communication ranges beyond the typical 300–500 meters of ITS-G5. Additionally, 5G NR-V2X introduces advanced features such as ultra-reliable low-latency communication (URLLC), network slicing, and support for edge computing, making it more suitable for emerging use cases including remote driving, cooperative perception, and high-definition map sharing.

Despite these benefits, C-V2X systems may suffer from higher base latency in scenarios involving network scheduling, signaling, and routing through the cellular core network. Moreover, the deployment of 5G infrastructure is still ongoing, and full nationwide coverage—especially in rural regions—may take several years to achieve. ITS-G5, by contrast, is already deployed in multiple pilot and pre-commercial projects across Europe and offers an open, infrastructure-independent model capable of operating effectively in areas without cellular coverage.

Interoperability remains a major concern. ITS-G5 and C-V2X are not inherently compatible at the physical or MAC layers, meaning that vehicles equipped with different technologies cannot directly communicate without dual-radio platforms or protocol gateways. This technological fragmentation could hinder the seamless operation of V2X services during the transition period in which both technologies coexist. As a result, hybrid V2X architectures that combine ITS-G5 and cellular V2X are increasingly considered a pragmatic and future-proof solution, leveraging the low-latency broadcast capabilities of ITS-G5 together with the scalability and wide-area coverage of cellular networks [22].

In summary, the choice between ITS-G5 and C-V2X depends on application requirements, deployment environment, and regulatory considerations. ITS-G5 excels in ultra-low-latency, short-range applications in decentralized settings, whereas C-V2X offers greater scalability, coordination, and integration potential with cloud-based and infrastructure-driven services. Hybrid models that leverage the complementary strengths of both technologies are therefore likely to play a central role in future intelligent transportation systems.
# 6 Conclusion

Delay performance is critical in ITS-G5-based Vehicular Ad Hoc Networks (VANETs), especially for safety-critical applications where timely message delivery can directly impact the effectiveness of collision avoidance systems and cooperative driving functions. While ITS-G5 offers a robust framework for short-range, low-latency communication, several factors significantly influence its delay characteristics.

At the MAC layer, the use of CSMA/CA introduces non-deterministic delays due to contention and collisions, particularly in dense traffic scenarios. In parallel, high vehicle mobility and rapidly changing topologies contribute to routing instability and increased end-to-end delay, while urban environments with physical obstructions and multipath effects further degrade wireless link quality.

To address these challenges, a range of delay mitigation strategies have been developed. Decentralized Congestion Control (DCC) mechanisms dynamically regulate transmission parameters to prevent channel saturation, while adaptive beaconing adjusts the frequency of Cooperative Awareness Messages (CAMs) based on contextual relevance, reducing unnecessary traffic. Additionally, hybrid architectures combining ITS-G5 with cellular technologies such as LTE-V2X or 5G NR-V2X offer a promising path toward enhanced scalability, coverage, and resilience, leveraging the strengths of both direct and infrastructure-assisted communication.

Comparative analyses highlight that while ITS-G5 excels in low-latency, infrastructure-free settings, C-V2X technologies provide better coordination and resource allocation under network support. However, full interoperability remains an open issue, and hybrid solutions may be essential in the near term to bridge technological gaps.

Future work should prioritize large-scale simulations and real-world field tests to validate delay models and evaluate the effectiveness of proposed optimizations in diverse traffic and environmental conditions. Emerging approaches such as machine learning-based adaptation, predictive congestion control, and context-aware communication policies also offer new avenues to improve delay performance dynamically.

Ultimately, achieving consistent, low-latency communication in VANETs requires a holistic approach that integrates adaptive networking strategies, cross-layer optimization, and robust system design. Such efforts will be essential to support the next generation of Cooperative Intelligent Transport
Systems (C-ITS) and pave the way toward safer and more efficient road transport.

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