ADAS – Collision Avoidance System
1. Introduction
Advanced Driver Assistance Systems (ADAS) represent a key step toward safer and more intelligent vehicles. These systems are designed to support the driver, reduce accident risk, and compensate for human limitations. Among the various ADAS features, the Collision Avoidance System (CAS) plays a vital role in preventing or mitigating traffic accidents.
By continuously observing the surrounding environment, a collision avoidance system can identify potential hazards, assess collision risk, and either warn the driver or automatically intervene when necessary. As a result, CAS serves as a fundamental building block for higher levels of driving automation.
2. Overview of Collision Avoidance Systems
A Collision Avoidance System is an integrated vehicle safety solution that aims to minimize both the probability and severity of collisions. It combines multiple sensors, real-time data processing, and vehicle control mechanisms to actively monitor driving conditions.
At a high level, the system performs the following core functions:
- Sensing and interpreting the surrounding environment
- Analyzing traffic situations and predicting collision risks
- Providing driver alerts or initiating automatic corrective actions
Environmental Perception
To achieve situational awareness, the system relies on a range of sensors such as cameras, radar, LiDAR, and ultrasonic devices. These sensors detect surrounding objects—including vehicles, pedestrians, cyclists, and static obstacles—while estimating distance, relative velocity, and motion direction.
Collision Risk Assessment
Sensor data is processed by embedded algorithms that track object motion and compute risk indicators such as time-to-collision. Based on this analysis, the system determines whether a potential collision is likely to occur.
Driver Alerts and Autonomous Actions
When a hazardous situation is identified, the driver is first notified through visual, acoustic, or haptic warnings. If the driver response is insufficient or delayed, the system can intervene autonomously by applying braking, steering support, or power reduction.
- Forward Collision Warning (FCW): Notifies the driver when closing in on a vehicle or obstacle too quickly.
- Automatic Emergency Braking (AEB): Automatically activates braking to avoid or reduce impact severity.
- Pedestrian and Cyclist Detection: Enhances protection for vulnerable road users, particularly in urban traffic.
- Rear and Cross-Traffic Collision Prevention: Assists in avoiding collisions from behind or lateral directions.
3. Sensor Technologies
3.1 Camera-Based Sensors
Camera sensors provide visual information by capturing detailed images of the vehicle’s surroundings. They are essential for object recognition and scene interpretation.
- Identify lanes, traffic signs, vehicles, and pedestrians
- Implemented using mono or stereo vision systems
- Cost-effective but sensitive to lighting and weather variations
3.2 Radar Sensors
Radar sensors operate by transmitting radio waves and analyzing the reflected signals to detect objects and measure their distance and speed. Due to their robustness, radar sensors are widely used in ADAS applications.
Operating in the millimeter-wave frequency range (typically 24 GHz, 77 GHz, or 79 GHz), radar systems perform reliably in challenging conditions such as rain, fog, snow, or low-light environments.
Operating Principle
The radar transmitter emits electromagnetic waves that reflect off surrounding objects. By measuring signal delay and frequency shifts, the system calculates object distance, relative velocity, and angle.
- Range estimation using time-of-flight
- Velocity measurement via Doppler shift
- Angle estimation using antenna arrays
Advantages
- Strong performance in adverse weather
- Accurate relative speed estimation
- Long detection range
Limitations
- Limited object classification capability
- Lower spatial resolution compared to LiDAR
- Susceptible to interference and multipath effects
Radar sensors are most effective when combined with other sensing modalities through sensor fusion, resulting in a reliable and redundant perception system.
3.3 LiDAR Sensors
LiDAR sensors use laser pulses to generate high-resolution three-dimensional representations of the environment.
- Highly accurate depth and shape perception
- Commonly used in advanced ADAS and autonomous vehicles
- Higher cost and complexity compared to cameras and radar
3.4 Ultrasonic Sensors
Ultrasonic sensors are designed for short-range object detection and are primarily used at low speeds.
- Effective for parking and close-proximity maneuvers
- Limited range but high reliability at low speeds
4. Sensor Fusion
Since no single sensor can provide complete and reliable perception under all conditions, collision avoidance systems employ sensor fusion. This approach integrates data from multiple sensors to enhance accuracy and robustness.
- Improved detection reliability
- Redundancy for safety-critical operations
- Enhanced performance in complex environments
5. Collision Avoidance System Architecture
5.1 Perception Layer
This layer processes raw sensor signals to detect and track objects in the vehicle’s vicinity.
- Computer vision and deep learning techniques
- Radar signal processing
- Object detection and classification
5.2 Fusion Layer
Outputs from individual sensors are combined using estimation and filtering techniques such as:
- Kalman Filters
- Extended Kalman Filters (EKF)
- Particle Filters
5.3 Prediction and Decision Layer
This layer estimates future object trajectories and evaluates collision risk.
- Time-to-Collision calculations
- Risk evaluation models
- Decision logic for warnings and interventions
5.4 Control and Actuation Layer
When risk thresholds are exceeded, the system activates appropriate responses.
- Visual, acoustic, or haptic alerts
- Automatic braking
- Steering assistance in advanced systems
6. Commonly Used Algorithms
- Object Detection: YOLO, SSD, Faster R-CNN
- Object Tracking: Kalman Filter, Multi-Object Tracking
- Collision Risk Estimation: Time-to-Collision, probabilistic models
- Vehicle Control: PID and Model Predictive Control
7. Practical Example
Consider a vehicle traveling at 60 km/h in an urban environment when a pedestrian suddenly enters the roadway. The camera detects the pedestrian while the radar measures distance and relative speed. Sensor fusion confirms a critical collision risk with a time-to-collision below a safe threshold.
If the driver fails to respond in time, the system automatically applies emergency braking, reducing speed or stopping the vehicle to prevent impact.
8. Benefits
- Reduced frequency and severity of collisions
- Enhanced driver awareness
- Essential foundation for automated driving
9. Challenges and Limitations
- Performance degradation in extreme conditions
- False alarms or missed detections
- High system complexity and validation effort
10. Future Developments
- End-to-end AI-based perception systems
- Integration with Vehicle-to-Everything (V2X)
- Progression toward higher automation levels
No comments:
Post a Comment