Numerous groups have applied a variety of deep learning techniques to computer vision problems in highway perception scenarios. In this paper, we presented a number of empirical evaluations of recent deep learning advances. Computer vision, combined with deep learning, has the potential to bring about a relatively inexpensive, robust solution to autonomous driving. To prepare deep learning for industry uptake and practical applications, neural networks will require large data sets that represent all possible driving environments and scenarios. We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection. We show how existing convolutional neural networks (CNNs) can be used to perform lane and vehicle detection while running at frame rates required for a real-time system. Our results lend credence to the hypothesis that deep learning holds promise for autonomous driving.
@cite_0: A self-driving car, to be deployed in real-world driving environments, must be capable of reliably detecting and effectively tracking of nearby moving objects. This paper presents our new, moving object detection and tracking system that extends and improves our earlier system used for the 2007 DARPA Urban Challenge. We revised our earlier motion and observation models for active sensors (i.e., radars and LIDARs) and introduced a vision sensor. In the new system, the vision module detects pedestrians, bicyclists, and vehicles to generate corresponding vision targets. Our system utilizes this visual recognition information to improve a tracking model selection, data association, and movement classification of our earlier system. Through the test using the data log of actual driving, we demonstrate the improvement and performance gain of our new tracking system.
@cite_1: This paper aims at real-time in-car video analysis to detect and track vehicles ahead for safety, autodriving, and target tracing. This paper describes a comprehensive approach to localizing target vehicles in video under various environmental conditions. The extracted geometry features from the video are continuously projected onto a 1-D profile and are constantly tracked. We rely on temporal information of features and their motion behaviors for vehicle identification, which compensates for the complexity in recognizing vehicle shapes, colors, and types. We probabilistically model the motion in the field of view according to the scene characteristic and the vehicle motion model. The hidden Markov model (HMM) is used to separate target vehicles from the background and track them probabilistically. We have investigated videos of day and night on different types of roads, showing that our approach is robust and effective in dealing with changes in environment and illumination and that real-time processing becomes possible for vehicle-borne cameras.
@cite_4: Detecting cars in real-world images is an important task for autonomous driving, yet it remains unsolved. The system described in this paper takes advantage of context and scale to build a monocular single-frame image-based car detector that significantly outperforms the baseline. The system uses a probabilistic model to combine multiple forms of evidence for both context and scale to locate cars in a real-world image. We also use scale filtering to speed up our algorithm by a factor of 3.3 compared to the baseline. By using a calibrated camera and localization on a road map, we are able to obtain context and scale information from a single image without the use of a 3D laser. The system outperforms the baseline by an absolute 9.4 in overall average precision and 11.7 in average precision for cars smaller than 50 pixels in height, for which context and scale cues are especially important.
@cite_12: A novel system for detection and tracking of vehicles from a single car-mounted camera is presented. The core of the system are high-performance vision algorithms: the WaldBoost detector [1] and the TLD tracker [2] that are scheduled so that a real-time performance is achieved. The vehicle monitoring system is evaluated on a new dataset collected on Italian motorways which is provided with approximate ground truth (GT0) obtained from laser scans. For a wide range of distances, the recall and precision of detection for cars are excellent. Statistics for trucks are also reported. The dataset with the ground truth is made public.
Recently, computer vision has been expected to player a larger role within autonomous driving. However, due to its history of relatively low precision, it is typically used in conjunction with either other sensors or other road models @cite_0 @cite_4 @cite_12 @cite_1 . Cho @cite_0 uses multiple sensors, such as LIDAR, radar, and computer vision for object detection. They then fuse these sensors together in a Kalman filter using motion models on the objects. Held @cite_4 , uses only a deformable parts based model on images to get the detections, then uses road models to filter out false positives. Carafii @cite_12 uses a WaldBoost detector along with a tracker to generate pixel space detections in real time. Jazayeri @cite_1 relies on temporal information of features for detection, and then filters out false positives with a front-view motion model.