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Self-Driving Cars: A Survey

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arxiv 1901.04407 v2 pith:56TGJJPE submitted 2019-01-14 cs.RO

classification cs.RO
keywords systemself-drivingcarsautonomyarchitecturedevelopedperceptionplanning
verification ladder T0 review T1 audit T2 compute T3 formal
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We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system that can be categorized as SAE level 3 or higher. The architecture of the autonomy system of self-driving cars is typically organized into the perception system and the decision-making system. The perception system is generally divided into many subsystems responsible for tasks such as self-driving-car localization, static obstacles mapping, moving obstacles detection and tracking, road mapping, traffic signalization detection and recognition, among others. The decision-making system is commonly partitioned as well into many subsystems responsible for tasks such as route planning, path planning, behavior selection, motion planning, and control. In this survey, we present the typical architecture of the autonomy system of self-driving cars. We also review research on relevant methods for perception and decision making. Furthermore, we present a detailed description of the architecture of the autonomy system of the self-driving car developed at the Universidade Federal do Esp\'irito Santo (UFES), named Intelligent Autonomous Robotics Automobile (IARA). Finally, we list prominent self-driving car research platforms developed by academia and technology companies, and reported in the media.

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  1. VISP: Volatility Informed Stochastic Projection for Adaptive Regularization

    cs.LG 2025-09 reject novelty 6.0 of 10

    VISP applies gradient-volatility-scaled stochastic projection to activations and reports improved test error on three image benchmarks, but without error bars or code.

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