Towards Full Automated Drive in Urban Environments: A Demonstration in GoMentum Station, California

Each year, millions of motor vehicle traffic accidents all over the world cause a large number of fatalities, injuries and significant material loss. Automated Driving (AD) has potential to drastically reduce such accidents. In this work, we focus on the technical challenges that arise from AD in urban environments. We present the overall architecture of an AD system and describe in detail the perception and planning modules. The AD system, built on a modified Acura RLX, was demonstrated in a course in GoMentum Station in California. We demonstrated autonomous handling of 4 scenarios: traffic lights, cross-traffic at intersections, construction zones and pedestrians. The AD vehicle displayed safe behavior and performed consistently in repeated demonstrations with slight variations in conditions. Overall, we completed 44 runs, encompassing 110km of automated driving with only 3 cases where the driver intervened the control of the vehicle, mostly due to error in GPS positioning. Our demonstration showed that robust and consistent behavior in urban scenarios is possible, yet more investigation is necessary for full scale roll-out on public roads.Comment: Accepted to Intelligent Vehicles Conference (IV 2017

Automotive sensor fusion systems for traffic aware adaptive cruise control

The autonomous driving (AD) industry is advancing at a rapid pace. New sensing technology for tracking vehicles, controlling vehicle behavior, and communicating with infrastructure are being added to commercial vehicles. These new automotive technologies reduce on road fatalities, improve ride quality, and improve vehicle fuel economy. This research explores two types of automotive sensor fusion systems: a novel radar/camera sensor fusion system using a long shortterm memory (LSTM) neural network (NN) to perform data fusion improving tracking capabilities in a simulated environment and a traditional radar/camera sensor fusion system that is deployed in Mississippi State’s entry in the EcoCAR Mobility Challenge (2019 Chevrolet Blazer) for an adaptive cruise control system (ACC) which functions in on-road applications. Along with vehicles, pedestrians, and cyclists, the sensor fusion system deployed in the 2019 Chevrolet Blazer uses vehicle-to-everything (V2X) communication to communicate with infrastructure such as traffic lights to optimize and autonomously control vehicle acceleration through a connected corrido

RobustStateNet: Robust ego vehicle state estimation for Autonomous Driving

Control of an ego vehicle for Autonomous Driving (AD) requires an accurate definition of its state. Implementation of various model-based Kalman Filtering (KF) techniques for state estimation is prevalent in the literature. These algorithms use measurements from IMU and input signals from steering and wheel encoders for motion prediction with physics-based models, and a Global Navigation Satellite System(GNSS) for global localization. Such methods are widely investigated and majorly focus on increasing the accuracy of the estimation. Ego motion prediction in these approaches does not model the sensor failure modes and assumes completely known dynamics with motion and measurement model noises. In this work, we propose a novel Recurrent Neural Network (RNN) based motion predictor that parallelly models the sensor measurement dynamics and selectively fuses the features to increase the robustness of prediction, in particular in scenarios where we witness sensor failures. This motion predictor is integrated into a KF-like framework, RobustStateNet that takes a global position from the GNSS sensor and updates the predicted state. We demonstrate that the proposed state estimation routine outperforms the Model-Based KF and KalmanNet architecture in terms of estimation accuracy and robustness. The proposed algorithms are validated in the modified NuScenes CAN bus dataset, designed to simulate various types of sensor failures

Multi-Lane Perception Using Feature Fusion Based on GraphSLAM

An extensive, precise and robust recognition and modeling of the environment is a key factor for next generations of Advanced Driver Assistance Systems and development of autonomous vehicles. In this paper, a real-time approach for the perception of multiple lanes on highways is proposed. Lane markings detected by camera systems and observations of other traffic participants provide the input data for the algorithm. The information is accumulated and fused using GraphSLAM and the result constitutes the basis for a multilane clothoid model. To allow incorporation of additional information sources, input data is processed in a generic format. Evaluation of the method is performed by comparing real data, collected with an experimental vehicle on highways, to a ground truth map. The results show that ego and adjacent lanes are robustly detected with high quality up to a distance of 120 m. In comparison to serial lane detection, an increase in the detection range of the ego lane and a continuous perception of neighboring lanes is achieved. The method can potentially be utilized for the longitudinal and lateral control of self-driving vehicles

Deep Learning Assisted Intelligent Visual and Vehicle Tracking Systems

Sensor fusion and tracking is the ability to bring together measurements from multiple sensors of the current and past time to estimate the current state of a system. The resulting state estimate is more accurate compared with the direct sensor measurement because it balances between the state prediction based on the assumed motion model and the noisy sensor measurement. Systems can then use the information provided by the sensor fusion and tracking process to support more-intelligent actions and achieve autonomy in a system like an autonomous vehicle. In the past, widely used sensor data are structured, which can be directly used in the tracking system, e.g., distance, temperature, acceleration, and force. The measurements\u27 uncertainty can be estimated from experiments. However, currently, a large number of unstructured data sources can be generated from sensors such as cameras and LiDAR sensors, which bring new challenges to the fusion and tracking system. The traditional algorithm cannot directly use these unstructured data, and it needs another method or process to “understand” them first. For example, if a system tries to track a particular person in a video sequence, it needs to understand where the person is in the first place. However, the traditional tracking method cannot finish such a task. The measurement model for unstructured data is usually difficult to construct. Deep learning techniques provide promising solutions to this type of problem. A deep learning method can learn and understand the unstructured data to accomplish tasks such as object detection in images, object localization in LiDAR point clouds, and driver behavior prediction from the current traffic conditions. Deep-learning architectures such as deep neural networks, deep belief networks, recurrent neural networks, and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, and machine translation, where they have produced results comparable with human expert performance. How to incorporate information obtained via deep learning into our tracking system is one of the topics of this dissertation. Another challenging task is using learning methods to improve a tracking filter\u27s performance. In a tracking system, many manually tuned system parameters affect the tracking performance, e.g., the process noise covariance and measurement noise covariance in a Kalman Filter (KF). These parameters used to be estimated by running the tracking algorithm several times and selecting the one that gives the optimal performance. How to learn the system parameters automatically from data, and how to use machine learning techniques directly to provide useful information to the tracking systems are critical to the proposed tracking system. The proposed research on the intelligent tracking system has two objectives. The first objective is to make a visual tracking filter smart enough to understand unstructured data sources. The second objective is to apply learning algorithms to improve a tracking filter\u27s performance. The goal is to develop an intelligent tracking system that can understand the unstructured data and use the data to improve itself

Extended Object Tracking: Introduction, Overview and Applications

This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of the extended object tracking problem and discuss its delimitation to other types of object tracking. Next, different aspects of extended object modelling are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking approaches - the random matrix approach and the Kalman filter-based approach for star-convex shapes. The next part treats the tracking of multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.Comment: 30 pages, 19 figure

Implementation of Vision and Lidar Sensor Fusion Using Kalman Filter Algorithm

Self-driving car is the next milestone of the automation industry. To achieve the level of autonomy expected in a self-driving car, the vehicle needs to be mounted with an assortment of sensors that can help the vehicle to perceive its three dimensional environment better which leads to better decision-making and control of the vehicle. Each sensor possesses different strengths and weaknesses; they can complement each other better when combined. This is done by a technique called sensor fusion wherein data from various sensors are put together in order to enhance the meaning and accuracy of the overall information. In real time implementations, uncertainty in factors that affect the vehicle's motion can lead to overshoot in parameters. In order to avoid that, an estimation filter is used to predict and update the fused values. This project focuses on sensor fusion of Lidar and Vision sensor (camera) followed by estimation using Kalman filter using values available from an online data set. It can be seen how the use of an estimation filter can significantly improve the accuracy in tracking the path of an obstacle