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Research interests in autonomous driving have increased significantly in recent years. Several methods are being suggested for performance optimization of autonomous vehicles. However, weather conditions such as rain, snow, and fog may hinder the performance of autonomous algorithms. It is therefore of great importance to study how the performance/efficiency of the underlying scene understanding algorithms vary with such adverse scenarios. Semantic segmentation is one of the most widely used scene-understanding techniques applied to autonomous driving. In this work, we study the performance degradation of several semantic segmentation algorithms caused by rain for off-road driving scenes. Given the limited availability of datasets for real-world off-road driving scenarios that include rain, we utilize two types of synthetic datasets.

EcoCAR 2: Plugging In to the Future is a three-year design competition co-sponsored by General Motors and the Department of Energy. Mississippi State University has designed a plug-in hybrid powertrain for a 2013 Chevrolet Malibu vehicle platform. This vehicle will be capable of 57 miles all-electric range and utility-factor corrected fuel economy of greater than 80 miles per gallon gasoline equivalent (mpgge). All modifications are designed without sacrificing any of the vehicle's utility or performance. Advanced modeling, simulation, and Hardware-in-the-Loop (HIL) simulation capabilities are being used for rapid control prototyping and vehicle design to ensure success in the following years of the competition.

A dynamic modeling framework was established to predict status (position, displacement, velocity, acceleration, and shape) of a towed vehicle system with different driver inputs. This framework consists of three components: (1) a state space model to decide position and velocity for the vehicle system based on Newton’s second law; (2) an angular acceleration transferring model, which leads to a hypothesis that the each towed unit follows the same path as the towing vehicle; and (3) a polygon model to draw instantaneous polygons to envelop the entire system at any time point. Input parameters of this model include initial conditions of the system, real-time locations of a reference point (e.g. front center of the towing vehicle) that can be determined from a beacon and radar system, and instantaneous accelerations of this system, which come from driver maneuvers (accelerating, braking, steering, etc.) can be read from a data acquisition system installed on the towing vehicle.