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AI for Good-Innovate for Impact



               Use case –13: Intelligent Automobile Integrated Safety and Secure

               Service Platform                                                                                     13-Changan








               Country: China

               Organization: Chongqing Changan Automobile

               Contact person: Dr. Yonggang Luo, luoyg3@ changan .com .cn


               13�1� Use case summary table

                Domain               Transportation
                The Problem to be    Enhance the safety and security level of autonomous driving, reduce
                addressed            traffic accidents, ensure the safety of life, and increase the degree of
                                     traffic automation.

                Key aspects of the   Integrated perception-decision closed-loop simulation testing
                solution             platform, achieving closed-loop testing in all scenarios through
                                     generative and reinforcement methods.

                Technology keywords  Autonomous driving, simulation, perception-decision safety, feder-
                                     ated learning, data barrier

                Data availability    Partially available.
                Metadata (type of    POI (point of interest) data; Object detection data; BEV images data;
                data)                Adversarial patches data; trajectory data
                Pipeline             Data collection and pre-processing; scenario generation; inference;
                                     evaluation; Feedback loop for continuous improvement
                Case Studies         Analysis of safety of perception-decision system through predictive
                                     simulations.

                Testbeds or pilot    Virtual simulations in controlled environments; Real-vehicle adversar-
                deployments          ial attack Tests; Real-world pilot projects in selected traffic scenarios;
                                     Ablation comparison test.
                Metrics, KPIs,       Reduction in accident rates; Adversarial efficiency compared to natu-
                measurements         ral data; Data utilization rate; Perception attack success rate.
                GPU                  Internal GPUs.

                Network require-     4g/5g network for collaborative training/testing, cloud service, auton-
                ments, architecture   omous driving simulator
                components
                Role of Trainings,   Developed the platform based on IEEE SA P3129, P3187, CSAE
                standards            standards and discussed related technical topics with experts in
                                     workshops (e.g DIAVT 2024)

                Pre-standard research Benchmarks on advanced autonomous driving simulation and testing
                                     algorithms & tools; Benchmarks on cross domain federated learning;





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