# Smart Roads for Autonomous Accident Detection and Warnings
## 2 Related Works
### Stand-Alone Approaches  
A system based on a programmable integrated circuit (PIC) microcontroller and an ultrasonic sensor installed in vehicles was proposed by [PERSON] and [PERSON] [5]. The ultrasonic sensor's role is to detect any obstruction, speed bump, steep curve, or humans on the road. The sensor's input is processed by the microcontroller, which acts as a computing device. If an abnormality is discovered, vehicle drivers are notified so they can take appropriate action, such as slowing down or changing lanes. The time delay is controlled by the microprocessor, and ultrasonic sensors measure the distance by using an echo produced by an impediment to a high-frequency sound pulse. A voice message is created if the distance between the vehicle and the obstacle is less than a preset threshold. [PERSON] et al. [6] proposed model-based visual sensing for avoidance of vehicular accidents. Image processing is implemented for detection of a nearby vehicle to maintain a safe distance by alerting the driver. In their work, a backend server monitors the behavior of various advanced vehicles through sensors and mobile communications. All the calculations and estimations are performed on the server. When the server finds any chance of a collision, it provides alerts to drivers. The proposed system is complicated and involves high computational costs. [PERSON] et al. proposed a technique that utilizes a short-range radar for self-driving vehicles by using normal distribution transform (NDT) [7]. The radar is used to predict lane changes with simultaneous localization and mapping (SLAM) and landmark extraction. A semi-Markov process is adopted to model the varying conditions and environments in a 2D representation. The main contribution of their work is a grid map representation where every cell's dynamics are modeled as a dynamic process. However, their proposed idea can solve the problem of localization only in a limited area. [PERSON] et al. [8] estimated the velocity and yaw rate (angular velocity during rotation) with a spatial-data radar sensor. They optimized the discrepancy in position from radar detection and Doppler velocity (to calculate the expected Doppler shift for existing pose hypothesis to each mapped landmark) for w.r.t. moving vehicles. The implementation of this approach in real systems is difficult due to high computation costs. Moreover, even small errors in methods that calculate relative motion between consecutive sensor readings will accumulate when summed over a longer time [9]. In order to drive safely, [PERSON] et al. [10] used cruise control technology in self-driving automobiles. In the cruise control system, the vehicle can detect and maintain a safe distance from the car ahead of it. Brakes and acceleration are controlled by using the proportional-integral-derivative (PID) approach, which calculates three basic coefficients to produce optimal responses. The PID approach also adjusts the accelerator and brake pedals to maintain a safe distance between vehicles. Distance errors caused by a preceding vehicle can be determined from distance information obtained by a laser scanner installed on the front of a vehicle. Acceleration control in each successive vehicle provides velocity control by transmitting acceleration position sensor (APS) signals, which can be artificially generated by the speed control unit to the electronic control unit (ECU). Their work was tested using simulators (Carsim and Simulink), and proved highly useful. An automated deep learning (DL)-based system [11] was developed for detecting accidents from video data. The system uses visual components in temporal order to represent traffic collisions. As a result, the model architecture is composed of a visual-features-extraction phase followed by transient pattern identification. Convolution and recurrent layers are used in the training phase to learn visual and temporal features.