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The rapid development of self-driving vehicles requires integrating a sophisticated sensing system to address the various obstacles posed by road traffic efficiently. While several datasets are available to support object detection in autonomous vehicles, it is crucial to carefully evaluate the suitability of these datasets for different weather conditions across the globe. In response to this requirement, we present a novel dataset named the Canadian Vehicle Datasets (CVD). Subsequently, we present deep learning models that use this dataset. The CVD comprises street-level videos which were recorded by Thales, Canada. These videos were collected with high-quality cameras mounted on a vehicle in the Canadian province of Quebec. The recordings were made during daytime and nighttime, capturing weather conditions such as hazy, snowy, rainy, gloomy, nighttime and sunny days. A total of 10000 images of vehicles and other road assets are extracted from the collected videos. A total of 8388 images were annotated with corresponding generated labels 27766 with their respective 11 different classes. We analyzed the performance of the YOLOv8 model trained using the existing RoboFlow dataset. Then, we compared it with the model trained on the expanded version of RoboFlow using the proposed weather-specific dataset, CVD. Final values of improved accuracy of 73.26 %, 72.84 %, and 73.47 % (Precision/Recall/mAP) were reported upon adding the proposed dataset. Finally, the model trained on this diverse dataset exhibits heightened robustness and proves highly beneficial for both autonomous and conventional vehicle operations, making it applicable not only in Canada but also in other countries with comparable weather conditions.

Optical code division multiple access (OCDMA) shows limitations in terms of the inefficient bandwidth utilization and low spectral density with one-dimensional (1D) codes. To overcome these limitations, a novel design is presented using a two dimensional (2D) spectral/spatial multiwavelength coding scheme. The proposed code is constructed using a 1D balanced incomplete block design (BIBD) technique. Theoretical and analytical results indicate that the proposed code provided improvement in the number of simultaneous users, code construction, and cross-correlation and minimized noise. Moreover, the bandwidth requirements can be minimized, and it can provide flexibility in addressing code sequences. Finally, results were compared with existing spectral-spatial 2D codes such as diagonal eigenvalue unity (DEU) and 2D diluted perfect difference (DPD). It was observed that the 2D-BIBD code fulfilled optical transmission needs with minimum effective source power (Psr = −27.5 dBm) when compared to 2D-DEU (−26.5 dBm) and 2D-DPD (−25.5 dBm) codes. Overall, our results suggested that the performance of BER for the proposed code was 72% and 22% higher than the existing 2D-DPD and 2D-DEU codes, respectively.

In this paper, we present a new algorithm to generate two-dimensional (2D) permutation vectors’ (PV) code for incoherent optical code division multiple access (OCDMA) system to suppress multiple access interference (MAI) and system complexity. The proposed code design approach is based on wavelength-hopping time-spreading (WHTS) technique for code generation. All possible combinations of PV code sets were attained by employing all permutations of the vectors with repetition of each vector weight (W) times. Further, 2D-PV code set was constructed by combining two code sequences of the 1D-PV code. The transmitter-receiver architecture of 2D-PV code-based WHTS OCDMA system is presented. Results indicated that the 2D-PV code provides increased cardinality by eliminating phase-induced intensity noise (PIIN) effects and multiple user data can be transmitted with minimum likelihood of interference. Simulation results validated the proposed system for an agreeable bit error rate (BER) of 10−9.