adaptive cruise control gif

Adaptive Cruise Control: Everything You Need to Know

Learn how adaptive cruise control can transform your driving experience with enhanced safety, convenience, and efficiency.

What You Need to Know About Adaptive Cruise Control

Driving can be fun, but let’s be honest: long trips on busy highways are a grind. The constant need to adjust your speed, stay vigilant, and deal with traffic can turn even the most scenic route into a stressful ordeal. Enter adaptive cruise control (ACC) .

This marvel of modern engineering promises to revolutionize driving, making journeys smoother, safer, and, dare we say, enjoyable.

Key Takeaways

• Understand How ACC Works: Learn how adaptive cruise control uses sensors and radar to adjust your car’s speed based on traffic conditions.
• Enhance Safety and Comfort: Discover how ACC reduces driver fatigue, maintains safe distances, and prevents rear-end collisions on the road.
• Prepare for the Future: Explore how ACC is paving the way for fully autonomous driving with advanced features and integration.

Let’s examine the ACC’s nitty-gritty, shall we? Adaptive Cruise Control isn’t just a fancy add-on for your car; it’s a groundbreaking technology that blends convenience with safety and is designed to revolutionize your driving experience.

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Table of Contents

What is adaptive cruise control?

• How does it work?
• Benefits of adaptive cruise
• Sensors and radars
• Control algorithms
• Integration with other systems
• Collision avoidance
• Lane-keeping assistance
• Stop-and-go functionality
• Pros and cons of adaptive cruise control
• Common myths about adaptive cruise
• Future cruise technology
• Frequently asked questions
• Final thoughts

Understanding Adaptive Cruise Control

What is adaptive cruise control.

Adaptive cruise control (ACC) is like regular cruise control but with a brain. Instead of maintaining a constant speed, ACC adjusts your car’s speed based on the traffic ahead. Radar and cameras can slow down your car if it detects a slower vehicle in front and speed up again when the road is clear. Think of it as cruise control on steroids.

How Does Adaptive Cruise Control Work?

ACC uses sensors, radars, and cameras to monitor the traffic around your vehicle. Here’s a simple breakdown:

• Sensors and Radars: Mounted on the front of the car, these detect the speed and distance of the vehicle ahead.
• Cameras: Help identify lane markings and obstacles.
• Control System: The car’s computer processes this data, adjusting the throttle and brakes accordingly.

Benefits of Adaptive Cruise Control

Adaptive cruise control isn’t just a fancy feature; it’s a game-changer. Here are some reasons why:

• Safety: Reduces the risk of rear-end collisions by maintaining a safe distance from the vehicle ahead.
• Comfort: Lessens driver fatigue on long journeys by handling speed adjustments.
• Fuel Efficiency: Optimizes speed and braking, leading to better fuel consumption.

The Mechanics Behind Adaptive Cruise Control

Sensors and radars.

The heart of ACC is its sensor and radar system. These components continuously scan the road ahead, ensuring your car maintains a safe distance from other vehicles. They work seamlessly, even in poor weather conditions.

Control Algorithms

Behind the scenes, complex algorithms are at play. These algorithms predict the movement of vehicles around you and adjust your car’s speed accordingly. It’s like having a mini-traffic controller in your vehicle.

Integration with Other Systems

ACC doesn’t work in isolation. It integrates with car systems like braking and engine control units (ECU). This integration ensures smooth and efficient operation, making your drive seamless.

Safety Features of Adaptive Cruise Control

Collision avoidance.

One of the primary safety benefits of ACC is collision avoidance. By maintaining a safe distance and automatically adjusting speed, ACC helps prevent rear-end collisions, among the most common types of accidents on highways.

Lane-Keeping Assistance

Some advanced ACC systems come with lane-keeping assistance. This feature ensures that your car stays within its lane, providing steering support if you begin to drift unintentionally. It’s like having a vigilant co-pilot.

Stop-and-Go Functionality

The stop-and-go feature is a lifesaver in heavy traffic. It allows your car to come to a complete stop when traffic halts and automatically resumes when traffic starts moving again. No more constant braking and accelerating—your car handles it for you.

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Pros and Cons of Adaptive Cruise Control

• Enhanced Safety: Reduced risk of collisions.
• Convenience: Less stress during long drives.
• Fuel Efficiency: Better fuel consumption through optimized driving.
• Cost: ACC can be pricey, especially in higher-end models.
• Learning Curve: It takes time to get used to the system.
• Dependency: Over-reliance on ACC might reduce driver attentiveness.

Common Myths About Adaptive Cruise Control

It replaces the driver.

One common misconception is that ACC can completely replace the driver. While it certainly aids driving, it doesn’t replace the need for an attentive driver. You still need to keep your hands on the wheel and be ready to take over at any moment.

It Only Works on Highways

Although ACC is most effective on highways, it’s not limited to them. Many systems are designed to work in various driving conditions, including city traffic. So, ACC has your back whether you’re on a long road trip or stuck in rush-hour traffic.

The Future of Adaptive Cruise Control

Integration with autonomous driving.

ACC is a step towards fully autonomous driving. As technology advances, we’ll likely see even more sophisticated systems requiring less human intervention. Who knows? Maybe one day, your car will drive you to work while you enjoy your morning coffee.

Enhanced Safety Features

Future ACC systems will likely have enhanced safety features, such as better pedestrian detection and response times. These advancements will make driving even safer and more convenient.

Adaptive cruise control is an advanced version that uses sensors and radar to adjust your car’s speed based on the traffic ahead.

How does adaptive cruise control enhance safety?

ACC enhances safety by maintaining a safe distance from the vehicle in front, reducing the risk of rear-end collisions, and sometimes integrating with lane-keeping assistance.

Can adaptive cruise control be used in city driving?

Many modern ACC systems are designed to work in various driving conditions, including city traffic and highways.

Is adaptive cruise control expensive?

ACC can increase a vehicle’s cost, especially in higher-end models, but many see it as a worthwhile investment for added safety and convenience.

Does adaptive cruise control replace the need for a driver?

No, ACC aids driving but does not replace the need for an attentive driver. You still need to monitor the road and be ready to take over if necessary.

What are the future developments for adaptive cruise control?

Future developments for ACC include better integration with autonomous driving technologies and enhanced safety features, such as improved pedestrian detection and faster response times.

Final Thoughts on Adaptive Cruise

Adaptive cruise control is a remarkable technology that makes driving safer, more comfortable, and more efficient. While it’s not a replacement for an attentive driver, it offers significant benefits that enhance the overall driving experience.

As technology continues to evolve, ACC will likely become even more advanced, bringing us closer to the era of fully autonomous vehicles.

So, the next time you’re on a long road trip, let ACC take some of the stress off your shoulders and enjoy the ride!

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Advanced Driver Assistance Systems (ADAS): Empowering Drivers

October 10, 2023 By 6 Comments

As vehicular technology continues to evolve at a rapid pace, the push towards safer, smarter, and more efficient driving experiences has been at the forefront of automotive innovation. Advanced Driver Assistance Systems ( ADAS ) is a key player in this technological revolution, and refers to a set of technologies and features integrated into modern vehicles to enhance driver safety, improve the driving experience, and assist in various driving tasks. It uses sensors, cameras, radar, and other technologies to monitor the vehicle’s surroundings, collect data, and provide real-time feedback to the driver. 

The roots of ADAS can be traced back to the early stages of automotive safety enhancements, such as the introduction of Anti-lock Braking Systems (ABS) in the late 1970s. However, the true emergence of ADAS as we understand it today began in the 2000s, with the integration of radar, cameras, and ultrasonic sensors.

Need for ADAS

Different levels of adas.

• What are the core components of ADAS?

ADAS in Action: How Does ADAS work?

What is the future of advanced driver assistance systems, conclusions.

In the current generation, there are multiple factors that require serious consideration:

Road Safety Statistics

• According to the World Health Organization (WHO) , approximately 1.35 million people die in road traffic accidents yearly, making it the eighth leading cause of death globally. 
• Road traffic crashes result in an estimated global economic loss of approximately 3% of GDP for most countries. This figure not only accounts for immediate medical costs but also the long-term impacts on productivity and rehabilitation costs.

ADAS can play a significant role in reducing these numbers. By integrating features like automatic emergency braking, lane departure warnings, and adaptive cruise control, the likelihood of collisions can be decreased.

Changing Urban Landscape

• With more people migrating to cities, the traffic density in urban areas is set to increase exponentially. This surge will be accompanied by new infrastructural developments and changing traffic patterns, creating more complex driving conditions.
• In many cities, road infrastructure hasn’t kept pace with the rate of urbanization. Narrow roads, inadequate signage, and unpredictable traffic patterns pose challenges to even the most experienced drivers.
• To combat these challenges, ADAS tools like traffic sign recognition, pedestrian detection, and parking assistance can prove invaluable.

Environmental Considerations

• Road vehicles are responsible for significant global CO2 emissions, which play a major role in climate change. Inefficient driving patterns, like rapid acceleration and deceleration, can exacerbate these emissions.
• In urban areas, frequent stop-and-go traffic leads to excessive fuel consumption and increased emissions. This not only has environmental repercussions but also has direct health implications due to the degradation of air quality.
• Systems like adaptive cruise control can modulate speed efficiently, leading to smoother driving patterns and reduced fuel consumption.

There are multiple levels of advanced driver assistance systems, based on the offerings:

Level 0 [Manual]: 

At this level, vehicles have no automation capabilities. The responsibility for control and decision-making rests entirely with the human driver.

Level 1 [Driver Assistance]: 

This level offers limited assistance, either with steering or with acceleration / braking. Examples include adaptive cruise control and lane-keep assistance, but the driver remains largely in control. Lane detection is a crucial aspect of driver assistance.

Level 2 [Partial Automation]: 

Vehicles at this level can manage both steering and acceleration/braking in certain conditions. However, the driver must always be alert and ready to take over, making sure to supervise the vehicle’s actions closely.

Level 3 [Conditional Automation]: 

Here, the vehicle can make dynamic decisions using AI components. It can handle some driving tasks but will prompt the driver to intervene when it encounters a scenario it can’t navigate.

Level 4 [High Automation]: 

These vehicles can operate in both urban and dedicated highway settings autonomously. Nevertheless, there are still conditions or situations where human intervention might be necessary.

Level 5 [Full Automation]: 

The ultimate level of automation, these vehicles don’t even need steering wheels or pedals. They’re designed to handle all driving tasks in every condition without any human input, except the final destination. This can also be a voice input.

What are the core components of ADAS? 

Long-range radar – continental ars640.

• Type: Long-Range
• Dimensions: 137 x 90 x 40 mm
• Weight: ~500 grams
• Measurement Parameters: Range, Doppler, Azimuth, and Elevation
• Operating Temperature Range: -40° to +85° Celsius
• Power Consumption: 12 volts

The ARS640 Long-Range Radar is a cutting-edge automotive radar system designed to support highly automated driving scenarios, ranging from Level 3 to Level 5 autonomous systems. It boasts a compact form factor with dimensions of 137 x 90 x 40 mm (excluding the connector) and a weight of approximately 500 grams. With an impressive range of 300 meters, it enables precise and direct measurement of four critical dimensions: range, doppler, azimuth, and elevation.

This radar system offers outstanding angular accuracy, with deviations of just ±0.1° in both azimuth and elevation, ensuring precise object detection and tracking. Its rapid update rate of 60 milliseconds enhances real-time awareness, while its operating temperature range of -40° to +85° Celsius ensures robust performance in diverse environmental conditions. With a low power dissipation of around 23 watts and a nominal supply voltage of 12 volts, it is an energy-efficient and reliable solution.

There are multiple applications for long-range radar in ADAS. Let’s explore a few: 

Adaptive Cruise Control (ACC) 

It is an advanced driver assistance system that automatically adjusts a vehicle’s speed to maintain a safe following distance from the vehicle ahead. Using long-range radar, and camera-based sensors, ACC continuously monitors the traffic environment and responds to changes in the flow. It intelligently decelerates the car accordingly when a slower-moving vehicle is detected in the same lane. Once the road ahead is clear, the system accelerates back to the preset speed, ensuring an optimal balance between maintaining speed and ensuring safety.

Short / Medium Range Radar – Continental SRR520

• Type: Short-Range
• Dimensions: 83 x 68 x 22 mm
• Weight: ~135 grams
• Additional Features: Object list management, blind spot warning, lane change assist (Type IIIc), rear-cross traffic alert with braking, front-cross traffic alert with braking, etc
• Operating Temperature Range: -40°C to +85°C Celsius

The SRR520 short range radar is a compact and highly capable radar system designed for automotive applications. With dimensions of 83 x 68 x 22 mm and a mass of approximately 135 grams (without connectors), it is a lightweight and space-efficient solution. Operating in the 76-77 GHz frequency range, it offers an impressive range of 100 meters at a 0° angle, along with a wide field of view, featuring ± 90° detection and ± 75° measurement.

This radar system boasts an exceptional update rate of 50 milliseconds and precise speed measurement accuracy of ± 0.07 kilometers per hour. It operates reliably in extreme temperatures ranging from -40°C to +85°C while dissipating 4.2 watts of power at a 12V supply voltage. Its advanced features include object list management, blind spot warning, lane change assist (Type IIIc), rear cross traffic alert with braking, front cross traffic alert with braking, rear pre-crash sensing, occupant safe exit support, and avoidance of lateral collisions.

There are multiple applications for short-medium range radar in ADAS. Let’s explore a few:

Cross Traffic Alert (CTA)

The Cross Traffic Alert (CTA) system becomes active when the driver shifts the vehicle into reverse. It employs short-to-medium range radar sensors, typically mounted on the rear corners of the vehicle, to scan for approaching vehicles from either side. If an approaching vehicle is detected, the system alerts the driver through visual, audible, or haptic warnings. These visual alerts often manifest in the side mirrors or on the vehicle’s infotainment screen. Based on this alert, the driver can stop, continue reversing cautiously, or wait for the cross traffic to clear.

Rear Collision Warning (RTW)

It uses a fusion of multiple rear cameras with radar technology to inform the driver if another vehicle is approaching from either direction, when the vehicle is moving in reverse or backing out of a parking space.

LiDAR: Light Detection and Ranging – InnovizTwo

• Type: 3D Point-cloud Estimation 
• Angular Resolution: 0.05°x0.05°
• Range of Frame Rate: 10, 15, 20 FPS
• Detection Range: 0.3 meters to 300 meters
• Maximum Field of View: 120°x43°

The InnovizTwo LiDAR system boasts impressive technical specifications, including a maximum angular resolution of 0.05°x0.05° and a configurable frame rate of 10, 15, or 20 FPS, making it a versatile and high-performance sensor for autonomous vehicles. With a detection range spanning from 0.3 meters to an impressive 300 meters, it excels in capturing objects at varying distances. Its expansive 120°x43° maximum field of view ensures comprehensive coverage of the vehicle’s surroundings.

InnovizTwo’s standout features include its ability to dynamically focus on four individually-controlled regions of interest within a limited FOV, enhancing visibility without compromising bandwidth, resolution, or frame rate. The system also excels at handling complex scenarios, recording multiple reflections per pixel and effectively processing laser pulses that encounter rain droplets, snowflakes, or multiple objects in their path.

Its scanning pattern is characterized by contiguous pixels, eliminating data gaps crucial for building a safe autonomous vehicle perception system. This design ensures the system’s capability to detect even small collision-relevant objects and pedestrians on the road surface. A vertical FOV offers high resolution evenly distributed across all regions, in contrast to other sensors that may prioritize the center and lose data towards the edges. This design provides more comprehensive data and accommodates mounting tolerances and varying driving conditions.

There are multiple applications for LiDAR in ADAS. Let’s explore a few:

Emergency Braking (ER)

It utilizes advanced sensor technologies, including radar, LiDAR, and high-resolution cameras, to continuously scan the vehicle’s forward environment. It employs sophisticated algorithms to process this data in real time, determining potential collision risks based on the relative speed and trajectory of detected objects. In situations where a potential frontal collision is imminent, and the driver fails to respond, the system automatically activates the braking mechanism, optimizing brake pressure to mitigate impact severity or, if possible, prevent the collision altogether.

Pedestrian Detection (PD)

PD uses LiDAR, which involves emitting laser beams and analyzing the reflected signals to identify and locate pedestrians in real-time. By capturing the 3D spatial information of a scene, LiDAR systems provide high-resolution data, enabling precise distance measurements and accurate differentiation of pedestrians from other objects in various lighting conditions.

Collision Avoidance (CA)

LiDAR technology plays a pivotal role in Collision Avoidance systems, offering high-resolution, 3D mapping of surroundings in real-time. By emitting laser beams and analyzing the reflected signals, LiDAR provides accurate distance measurements, enabling vehicles to detect obstacles and respond swiftly, even in congested traffic scenarios. This advanced sensing enhances the vehicle’s safety and decision-making capabilities in complex environments.

Cameras – Continental MFC500

• Type: Mono-Vision
• Dimensions: 88 x 70 x 38 mm
• Weight: ~200 grams
• Additional Features: Emergency braking, traffic sign assist, traffic jam assist, adaptive cruise control control, continuous lane centering, etc 
• Power Consumption: ~7 watts

The MFC500 Mono camera is a compact and lightweight automotive vision system with highly notable technical specifications. Measuring a mere 88 x 70 x 38mm and weighing less than 200g offers an unobtrusive form factor for seamless vehicle integration. The camera boasts an impressive, effective field of view, with horizontal coverage of up to 125° and vertical coverage of up to 60°, enabling comprehensive vision and perception capabilities for various driving scenarios. Operating within an extreme temperature range from -40° to +95° Celsius, it ensures reliable performance in harsh environmental conditions.

With a low power dissipation of less than 7 watts and a 12V supply voltage, the MFC500 is energy-efficient and compatible with automotive power systems. Its feature set aligns with key safety and regulatory standards, including support for General Safety Regulation (GSR 2.0) and EU-NCAP requirements. The camera offers a range of advanced driver assistance features, including emergency braking for vehicles, pedestrians, and cyclists, emergency lane keeping, speed limit information with automatic limitation, traffic sign assist, event data recording, adaptive cruise control with stop&go functionality and continuous lane centering assist. It also supports standard headlight systems up to matrix beams, enhancing nighttime visibility and safety. The MFC500 Mono camera is a powerful and versatile component for modern automotive safety and automation systems.

There are multiple applications for pure vision in ADAS. Let’s explore a few:

Traffic Sign Recognition (TSR)

Utilizing high-resolution cameras, these systems employ advanced image processing and deep learning algorithms to identify and interpret traffic signs and signals. Pure vision-based TSR provides real-time feedback by analyzing visual data, ensuring drivers or autonomous systems adhere to road regulations. The effectiveness of such systems is enhanced by adaptive lighting conditions and the capability to distinguish between diverse sign types, colors, and shapes.

Lane Departure Warning (LDW)

It provides dynamic alerts to the driver if the vehicle is drifting out of a specific lane on the road. LDW continuously monitors road lane markings to detect unintentional lane shifts. This technology, reliant on high-resolution cameras, processes the road’s visual data in real-time to ensure the vehicle remains within its designated path, enhancing road safety and assisting drivers in maintaining proper lane discipline.

Surround View (SV)

SV is a critical component of advanced driver assistance systems, enhancing driver safety and awareness by offering a comprehensive 180 to 360-degree view of the vehicle’s surroundings. This technology relies on the fusion of data from ultrawide-angle cameras strategically positioned on the vehicle to eliminate blind spots and provide a clear, real-time visual representation of the environment.

The first automobile manufacturer that comes to mind when someone says ‘Self-Driving Car’ is Tesla. This bleeding-edge feature called ‘Autopilot’ relies heavily on artificial intelligence and deep learning techniques to process and interpret the data from sensors. Some of the software components and techniques involved include:

Neural Networks

Tesla uses deep neural networks for image and data analysis. Convolutional Neural Networks (CNNs) are used to recognize and track objects, such as other vehicles, pedestrians, and lane markings.

Sensor Fusion

The system combines data from cameras, ultrasonic sensors, and radar to create a more comprehensive and accurate understanding of the vehicle’s surroundings. This sensor fusion is crucial for safe autonomous driving.

Mapping and Localization

Tesla vehicles use high-definition maps to help with localization. GPS data is combined with information from sensors to position the vehicle on the road precisely.

Control Algorithms

Sophisticated control algorithms are used to make driving decisions, such as maintaining the vehicle within lanes, changing lanes when necessary, and navigating complex traffic scenarios.

Technology is always evolving, so it is important to keep an eye on what the future holds for this specific technology.

Increased Automation

While Level 2 automation (e.g., Tesla’s Autopilot) was common in 2021, Level 3 and Level 4 systems, which require less driver intervention, are expected to become more prevalent.

Better Sensor Technology

The future of ADAS will likely involve the integration of more advanced sensors, higher-resolution cameras, and improved LiDAR and radar systems to provide a more accurate and comprehensive view of the vehicle’s surroundings.

User Experience and Human-Machine Interaction

The design of user interfaces and the interaction between humans and AI-driven vehicles will be critical to ADAS development. Natural language processing (NLP) and computer vision will play a role in creating more intuitive and user-friendly interfaces.

Environmental Impact

There will be a growing emphasis on reducing the environmental impact of transportation. ADAS may play a role in optimizing driving patterns for fuel efficiency and reducing emissions.

Advanced Driver Assistance Systems , uses industry-leading computer vision technology to represent a pivotal technological frontier in the automotive industry. Leveraging cutting-edge developments in artificial intelligence, deep learning, and sensor technology, it has transcended conventional driver assistance systems, evolving into a sophisticated suite of tools designed to enhance safety, automation, and user experience. The integration of high-resolution cameras, LiDAR, radar, and ultrasonic sensors, combined with the ever-advancing capabilities of neural networks and reinforcement learning algorithms, empowers ADAS to navigate complex real-world driving scenarios with unprecedented precision. 

At this point, we’re evolving well beyond the scope of driver assistance and into the domain of autonomous driving. But no matter how far our solutions advance, they’ll all owe their origins to the building blocks of our lifesaving core ADAS technology.

• World Health Organization Statistics: https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries
• Continental ARS640: https://www.continental-automotive.com/en-gl/Passenger-Cars/Autonomous-Mobility/Enablers/Radars/Long-Range-Radar/ARS640
• Continental SRR520: https://www.continental-automotive.com/en-gl/Passenger-Cars/Autonomous-Mobility/Enablers/Radars/Short-Range-Radar/SRR520
• Continental MFC500: https://www.continental-automotive.com/en-gl/Passenger-Cars/Autonomous-Mobility/Enablers/Cameras/Mono-Camera
• InnovizTwo LiDAR: https://innoviz.tech/innoviztwo
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adaptive-cruise-control

Here are 19 public repositories matching this topic..., meixinzhu / velocity_control.

Source code for paper "Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving"

• Updated Mar 24, 2023
• Jupyter Notebook

Marksman007577 / Autonomous-Drive

Autonomous Vehicle modelling using MATLAB and Simulink

• Updated May 10, 2023

kostaszaf / can-gateway-sniffer

An embedded CAN bus sniffer which is able to monitor any of the vehicle internal CAN bus and perform some action by triggering new CAN messages. In this way certain vehicle functionality can be triggered by responding to custom steering wheel button events, or use the vehicle virtual cockpit to display OBD-PIDs values instead of relying on an ex…

• Updated Nov 3, 2021

icns-distributed-cloud / adaptive-cruise-control

YOLO-v5 기반 "단안 카메라"의 영상을 활용해 차간 거리를 일정하게 유지하며 주행하는 Adaptive Cruise Control 기능 구현

• Updated Oct 28, 2021

kochlisGit / Autonomous-Vehicles-Adaptive-Cruise-Control

An implementation of an Autonomous Vehicle Agent in CARLA simulator, using TF-Agents

• Updated Nov 23, 2023

andresmendes / Truck-platooning---String-stability

Animation of a string stable four-truck platooning at 72 km/h with 1 second time gap.

• Updated May 17, 2021

resuldagdanov / self-improving-RL

Codebase of paper "Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms" published at ICRA 2023 📝

• Updated Jul 13, 2023

JohannesAutenrieb / CBF_ACC

This collection of MATLAB scripts intends to study the performance of state-constrained controllers utilizing control barrier functions in the context of adaptive cruise control.

• Updated Aug 3, 2023

andresmendes / Truck-platooning---Cut-in-scenario

Simulation and animation of a cut-in scenario between trucks one and two in a four-truck platoon with 1 second time gap.

• Updated Aug 21, 2021

alessandro-crescenzi / A-deep-approach-for-an-adaptive-cruise-control-system

This is an implementation of an adaptive cruise control system based on a computer vision pipeline. This work is based on YOLACT, a State-Of-The-Art real-time instance segmentation network. You're welcome to test and try our code, we hope you'll enjoy this work!

• Updated Nov 27, 2023

GabrieleCuni / Technologies_for_Autonomous_Vehicles

Projects done during T4AV: Lane Detection, Adaptive Cruise Control and Platooning

• Updated Aug 31, 2021

data-and-decision-lab / self-improving-RL

Codebase of paper "Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms" published at ICRA 2023

Amey-Thakur / ADAPTIVE-CRUISE-CONTROL

Adaptive Cruise Control in MATLAB

• Updated Mar 13, 2024

spsingh37 / SelfDriveSuite

This is a comprehensive project focused on developing advanced algorithms for autonomous driving, including adaptive cruise control, model predictive control (MPC), and robust scene understanding in various weather conditions.

• Updated Aug 17, 2024

kochlisGit / Noise-Adaptive-Driving-Assistance-System

Noise-Adaptive Driving Assistance System (NADAS) using Deep Reinforcement Learning, State-Estimation & State Representation

• Updated Oct 13, 2023

RalphHightower / openpilot

openpilot is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for over 200 supported car makes and models.

• Updated Aug 24, 2024

AlexandruRusu23 / Adaptive-Cruise-Control-UDOO

• Updated Mar 22, 2023

CtrlAltAli / Adaptive-Cruise-Control-Using-Arduino

Implement Adaptive Cruise Control Using Arduino Kit - Windsor university project

• Updated Apr 28, 2022

DavidePeron19 / Adaptive_Cruise_Control_System

Bachelor's Thesis Project (A.Y. 2021/2022) Supervisor Professor Valcher

• Updated Oct 5, 2023

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