LiDAR based prediction and contact based validation of crash parameters for a preemptive restraint strategy

During the event of a crash, total response time plays a critical role in determining the effectiveness of passive safety system. The response time of the system can be broken down into its components, which mainly comprises of sensor response time, data processing time, communication time and actuation time. A lot of research is done to effectively reduce the individual time components and hence improve the efficiency of passive safety system. But this approach has inherent limitations because these individual time parameters cannot be brought down significantly as it’s necessary to separate crash events from other events which may have similar stimuli to the sensors. A level of redundancy in the sensing algorithm is necessary to be highly certain about the event of a crash and other relevant parameters. Then, these parameters are to be processed to analyze the crash scenario in order to deploy an appropriate restraint strategy.In this research paper, we propose a novel approach of predicting the crash parameters that effectively describes a possible crash scenario before the actual crash occurs and then validating the parameters in order to check the certainty of the predictions. The key advantage of this approach is that we can considerably reduce the response time as the data processing and the most appropriate restraint strategy can be selected before T
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and as soon as the parameters are validated, the actuation trigger can be initiated.