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Abstract Artificial intelligence (AI) and edge computing have truly advanced in vehicular networks encouraging assessment of real‐time traffic conditions using kinetic information of autonomous vehicles with the help of road side units (RSUs). However, regardless of numerous improvements in sensor fusion technologies the existing vision/LIDAR‐based systems have found severe difficulties during perception on roads. In addition, the inter‐vehicular communications are hampered due to inefficient RSU placement techniques causing high‐latency issues during transmission of messages. Therefore, this article presents an AI‐driven vision‐based self driver assistance system (VSDAS) comprising a joint RSU deployment mechanism that utilizes enhanced memetic architecture‐based optimal RSU placement (MARP) method and an object detection model that implements an improved Haar‐cascade object detection algorithm for speedy identification of object. We have designed two varieties of genetic algorithm (GA) to solve optimal placement problem of RSUs: genetic architecture‐based with random restart hill climbing (GARRH) and the other is MARP for efficient placement of RSUs. After our experimental results, we see that the MARP algorithm shows best possible RSU locations over different generations achieving significantly better fitness scores than the GAHRC and GA ascribing to its local search process. In addition, Haar‐cascade achieves better mean average precision than local binary pattern and histogram of oriented gradients by selecting key frames. The experimental outcomes of our model reveals that the proposed enhanced memetic algorithm reduces the transmission delay to a greater extent. Additionally, the outcomes of our investigations on two public datasets (KITTI and Panasonic) showed that our improved algorithm clearly enhances the object detection performance.
Rawlley et al. (Tue,) studied this question.