# Smart Roads for Autonomous Accident Detection and Warnings
## 2 Related Works
### Stand-Alone Approaches
In public traffic accident datasets, accuracy of 98% was attained in detection of accidents, demonstrating a strong capacity for detection independent of the road structure. The solution is limited to automobile crashes, not motorbikes, bicycles, and pedestrians. Furthermore, the model makes mistakes when determining accident segments under poor illumination (e.g., at night), at low resolutions, and when there are occlusions. A system suggested by [PERSON] et al. [12] includes a LiDAR sensor to measure speed, and a prediction algorithm based on the stop sight distance (SSD) formula to calculate braking distance. Red light running (RLR) is predicted by a sensor that monitors the speed of the vehicle approaching an intersection. The results demonstrated that RLR can be predicted from the front and side of a moving vehicle using LiDAR and the SSD algorithm. The vehicle's weight is fixed in this method; however, in practice, the braking distance varies depending on the vehicle's weight. [PERSON] et al. [13] suggested a method for recognizing and tracking obstacles in 3D LiDAR point clouds. The point clouds generated by 3D LiDAR after road segmentation are first rasterized, and then additional relevant cells are added. A clustering technique is then used to group the obstacle cells in the next stage. Static obstacles are discovered using a multi-frame fusion approach by evaluating the clusters. Finally, moving obstacles are tracked using an upgraded dynamic tracking point model and a regular Kalman filter. The proposed method was tested in a variety of demanding settings, but has yet to deal with scenarios where barriers are extremely near one another. A systematic approach to multi-LiDAR data processing [14] was introduced, consisting of calibration, filtering, clustering, and classification. Within the multi-LiDAR architecture, the accuracy of obstacle detection is enhanced by using noise filtering and clustering. The applied filtering method is based on sampling point occupancy rates (Ors). An adaptive search (AS) approach is used to improve density-based spatial clustering of applications with noise (DBSCAN). Furthermore, when AS-DBSCAN is combined with the proposed OR-based filtering, more robust and precise obstacle detection is achieved. The agglomerative hierarchical clustering technique with complete linkage is applied. The same procedure is then employed with a single linkage. If the two targets occupy neighboring segments or share the same segment, and their longitudinal separation is less than 0.8 m, the clustering algorithm utilized might aggregate detection of vehicles into a cluster, resulting in a merged/unresolved report. An inside perception test and an on-road test were performed on a fully instrumented, autonomous hybrid electric automobile. Experiments proved that the proposed algorithms are reliable and practical. Using LiDAR, a mechanism for estimating the contour of approaching vehicles in the pre-crash phase was explored [15]. The data are initially combined, and an environmental model containing the identified items and their properties, such as position and shape, is computed. The mechanism extracts the main parameters of the vehicles from a LiDAR 3D point cloud through a convex hull algorithm. The relevant vehicle contour and longitudinal axis are then calculated. On both static and dynamic measures, tests on vehicles revealed good accuracy and durability of the estimation. Despite the fact that a convex hull algorithm examines a set of points from the reflection point clusters' outermost region, it defines its bounds by connecting two neighboring points with straight lines. The accuracy of contour prediction may be affected by the fact that a vehicle's contour is often a higher degree curve. Furthermore, due to noise-induced change in reflection points, evaluating individual points for analysis can introduce mistakes [16].