Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication are two emerging technologies that can potentially revolutionize transportation safety and efficiency.
V2V communication allows vehicles to exchange information with each other, such as their speed, location, and heading. This information can be leveraged to warn drivers of potential hazards, such as upcoming collisions or lane changes.
On the other hand, V2I communication allows vehicles to communicate with infrastructure elements, such as traffic lights and road signs. This information can serve to improve traffic flow and coordination.
The successful deployment of V2V and V2I communication relies on several factors, including interoperability, network security, and adherence to communication standards. That said, deep learning holds a lot of promise to make V2V and V2I communication systems more reliable and effective.
7 Interesting Deep Learning Applications in V2V and V2I Communication
Deep learning algorithms can be trained on large datasets of traffic data to learn how to identify and predict potential hazards. This information can then be used to warn drivers or to automatically take evasive action.
To that end, here are the seven most prominent deep learning applications in the V2V and V2I communication space:
1. Enhancing Collision Avoidance
The automotive collision avoidance systems market was valued at $41 billion in 2020 and is expected to reach $82 billion by 2026, registering a CAGR of 12.11% during the forecast period. The major growth drivers in the market are:
- The increasing demand for automated driving systems
- Rising concerns over road safety
- Advancements in camera technology
The surge in advanced driver assistance systems (ADAS) is also contributing to market growth, enabling new functionalities that help drivers make better decisions.
Enhancing collision avoidance using deep learning involves processing data from various sensors, such as cameras, LiDAR, radar, etc., to analyze the surrounding environment. Deep neural networks are trained to detect and classify objects in real-time, including other vehicles, pedestrians, and obstacles. The AI model predicts potential collision risks based on the relative position, velocity, and behavior of these objects.
2. Intelligent Traffic Signal Control
Deep neural networks analyze historical traffic patterns and current traffic flows to predict congestion and adjust signal timings accordingly. By dynamically adapting signal cycles, the system reduces waiting times at intersections, minimizes traffic congestion, and improves overall traffic flow. The
This application demonstrates how deep learning’s ability to process vast amounts of data in real-time can lead to more efficient urban transportation systems, saving commuter time and reducing carbon emissions.
It’s noteworthy though that such traffic signal control might not have a direct bearing with the information relayed to the vehicles. However, drivers can be informed about the signal patterns using traffic sign recognition. At the end of the day, vehicles equipped with V2I capabilities can immensely benefit from this.
3. Predictive Maintenance for Vehicle Health
Predictive maintenance in vehicles relies on deep learning algorithms to analyze sensor data collected from various vehicle components. These algorithms learn normal behavior patterns and detect anomalies that might indicate impending mechanical failures. By detecting these anomalies early, the system can alert drivers or maintenance teams to perform necessary repairs, preventing breakdowns and reducing maintenance costs.
The key thing to consider here is that deep learning’s capacity to handle complex sensor data and identify subtle deviations from the norm is crucial for timely maintenance interventions and ensuring vehicle safety and reliability.
4. Pedestrian and Cyclist Detection for Safety Enhancement
The US Department of Transportation outlines that pedestrian and bicyclist fatalities constitute a 19% share of all traffic fatalities every year. That’s certainly a serious concern and an area where the application of deep learning can work wonders for V2V and V2I communication.
Deep learning systems use real-time camera data to detect human shapes and movements, even in complex urban environments. In fact, deep neural networks can accurately differentiate pedestrians and cyclists from other objects, issuing alerts to drivers and even applying emergency braking if necessary. This is a reflection of deep learning’s capability to process visual data and make critical safety assessments to prevent accidents.
5. Advanced Parking Management with AI-driven Insights
A study that surveyed the parking data across 10 US cities revealed that searching for parking can accrue costs up to $73 billion a year — a problem that keeps compounding with more and more motorists embracing the roads.
Deep learning comes to the rescue here. Advanced parking management systems employ deep learning to monitor parking spaces using cameras. Deep neural networks analyze the camera feed to detect available parking spots, providing real-time information to drivers through mobile apps or digital displays. This improves urban parking efficiency by reducing the time drivers spend searching for parking spaces.
Also Read: What Is the Difference Between AI and Deep Learning?
6. Traffic Flow Prediction for Optimal Routing
Traffic flow prediction involves the use of deep learning to analyze historical traffic data, real-time conditions, and events that may impact road congestion. Deep neural networks model complex traffic patterns and predict areas likely to experience traffic jams or road closures.
Navigation systems can then provide drivers with optimal routes to avoid congested areas, saving time and minimizing the environmental impact of traffic congestion.
7. Blind Spot Detection
Deep learning-based blind spot detection systems use a combination of sensors and cameras to monitor a vehicle’s surroundings. These systems analyze the data to identify vehicles located in the blind spots of the driver’s field of vision. By alerting drivers about vehicles in adjacent lanes, these systems enhance lane-change safety and help prevent collisions.
Ascentt is a leading provider of data-driven services for the automotive industry. We automotive manufacturers harness the power of data to improve safety, efficiency, and customer experience. Contact us to learn more about how we can help you leverage deep learning for effective V2V and V2I communication.
