Data-driven autonomous driving motion generation tasks are frequently impacted by the limitations of dataset size and the domain gap between datasets, which precludes their extensive application in real-world scenarios. To address this issue, we introduce SMART, a novel autonomous driving motion generation paradigm that models vectorized map and agent trajectory data into discrete sequence tokens. These tokens are then processed through a decoder-only transformer architecture to train for the next token prediction task across spatial-temporal series. This GPT-style method allows the model to learn the motion distribution in real driving scenarios. SMART achieves state-of-the-art performance across most of the metrics on the generative Sim Agents challenge, ranking 1st on the leaderboards of Waymo Open Motion Dataset (WOMD), demonstrating remarkable inference speed. Moreover, SMART represents the generative model in the autonomous driving motion domain, exhibiting zero-shot generalization capabilities: Using only the NuPlan dataset for training and WOMD for validation, SMART achieved a competitive score of 0.71 on the Sim Agents challenge. Lastly, we have collected over 1 billion motion tokens from multiple datasets, validating the model's scalability. These results suggest that SMART has initially emulated two important properties: scalability and zero-shot generalization, and preliminarily meets the needs of large-scale real-time simulation applications. We have released all the code to promote the exploration of models for motion generation in the autonomous driving field.

With entrepreneurship today witnessing the advent of smart solutions, there have undoubtedly been numerous instances wherein issues plaguing the vast majority of the Indian society have found mitigation. On deeper analysis, the key behind the smart models are the data which is derived from what is available in society. Now, with data being the key criteria, Indian society has been witnessing the rise of numerous startups which are engrossed in crunching data to develop robust data-driven models such that a proper analysis of the issue can be undertaken to formulate smart solutions. Hence, data science becomes a smart part of today's smart entrepreneurship. With data sciences being the focal point, Entrepreneur India analyses why this new trend is now the essential tool for a majority of smart entrepreneurial solutions.

Opinions expressed by Entrepreneur contributors are their own. If you are a newbie entrepreneur looking at starting up a venture with aims of developing smart models for society, then it becomes imminent that the social sectors which could get disrupted to the maximum through your smart models are listed. In this regard, Entrepreneur India interacted with a multitude of industry individuals to decode on potentially disruptive sectors so that you could look at developing solutions by harnessing disruptive technology. Undoubtedly, Indian healthcare now deserves a much-required shot-in-the-arm as far as making facilities accessible and affordable to people across geographies and financial backgrounds are concerned. Here is where your startups could come into leverage technology like Blockchain, the Internet-of-Things ("IoT), Artificial Intelligence (AI), and Predictive Analytics.

This paper addresses the problem of inferring sparse causal networks modeled by multivariate auto-regressive (MAR) processes. Conditions are derived under which the Group Lasso (gLasso) procedure consistently estimates sparse network structure. The key condition involves a "false connection score." In particular, we show that consistent recovery is possible even when the number of observations of the network is far less than the number of parameters describing the network, provided that the false connection score is less than one. The false connection score is also demonstrated to be a useful metric of recovery in non-asymptotic regimes. The conditions suggest a modified gLasso procedure which tends to improve the false connection score and reduce the chances of reversing the direction of causal influence. Computational experiments and a real network based electrocorticogram (ECoG) simulation study demonstrate the effectiveness of the approach.