For human drivers, having rear and side-view mirrors is vital for safe driving. They deliver a more complete view of what is happening around the car. Human drivers also heavily exploit their mental map for navigation. Nonetheless, several methods have been published that learn driving models with only a front-facing camera and without a route planner. This lack of information renders the self-driving task quite intractable. We investigate the problem in a more realistic setting, which consists of a surround-view camera system with eight cameras, a route planner, and a CAN bus reader. In particular, we develop a sensor setup that provides data for a 360-degree view of the area surrounding the vehicle, the driving route to the destination, and low-level driving maneuvers (e.g. steering angle and speed) by human drivers. With such a sensor setup we collect a new driving dataset, covering diverse driving scenarios and varying weather illumination conditions. Finally, we learn a novel driving model by integrating information from the surround-view cameras and the route planner. Two route planners are exploited: 1) by representing the planned routes on OpenStreetMap as a stack of GPS coordinates, and 2) by rendering the planned routes on TomTom Go Mobile and recording the progression into a video. Our experiments show that: 1) 360-degree surround-view cameras help avoid failures made with a single front-view camera, in particular for city driving and intersection scenarios; and 2) route planners help the driving task significantly, especially for steering angle prediction.
In-car navigation systems have been widely used to show the vehicle’s current location on a map and to inform drivers on how to get from the current position to the destination. Increasing the accuracy and robustness of systems for positioning, navigation and digital maps has been another research focus for many years. Several methods for high-definition mapping have been proposed @cite_59 @cite_64 , some specifically for autonomous driving @cite_80 @cite_27 . Route planning has been extensively studied as well @cite_47 @cite_19 @cite_21 @cite_44 @cite_2 , mainly to compute the fastest, most fuel-efficient, or a customized trajectory to the destination through a road network. Yet, thus far their usage is mostly restricted to help human drivers. Their accessibility as an aid to learn autonomous driving models has been limited. This work reports on two ways of using two kinds of maps: a s-o-t-a commercial map TomTom Maps https: www.tomtom.com en_us drive maps-services maps and the excellent collaborative project OpenStreetMaps @cite_82 .
@cite_64 @cite_21 @cite_44 @cite_19 @cite_27 @cite_59 @cite_2 @cite_80 @cite_47 @cite_82
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Digital maps can provide essential information for many advanced driver assistance systems (ADAS) dedicated to both safety and comfort applications. As the level of detail and global accuracy of state-of-the-art digital maps are not sufficient for a multitude of applications, we present methods and models for the generation of high precision maps. The proposed modeling includes 3D lane level information, road markings, landmarks and additional attributes with benefits for many ADAS. The extensive use of circular arc splines enables both adjustable accuracy and high efficiency as our cartographic methodology guarantees the minimum number of curve segments with respect to a given error threshold. GPS devices have been widely used in automobiles to compute navigation routes to destinations. The generated driving route targets the minimal traveling distance, but neglects the sightseeing experience of the route. In this study, we propose an augmented GPS navigation system, GPSView, to incorporate a scenic factor into the routing. The goal of GPSView is to plan a driving route with scenery and sightseeing qualities, and therefore allow travelers to enjoy sightseeing on the drive. To do so, we first build a database of scenic roadways with vistas of landscapes and sights along the roadside. Specifically, we adapt an attention-based approach to exploit community-contributed GPS-tagged photos on the Internet to discover scenic roadways. The premise is: a multitude of photos taken along a roadway imply that this roadway is probably appealing and catches the public's attention. By analyzing the geospatial distribution of photos, the proposed approach discovers the roadside sight spots, or Points-Of-Interest (POIs), which have good scenic qualities and visibility to travelers on the roadway. Finally, we formulate scenic driving route planning as an optimization task towards the best trade-off between sightseeing experience and traveling distance. Testing in the northern California area shows that the proposed system can deliver promising results. Planning an itinerary before traveling to a city is one of the most important travel preparation activities. In this paper, we propose a novel framework called TripPlanner, leveraging a combination of location-based social network (i.e., LBSN) and taxi GPS digital footprints to achieve personalized, interactive, and traffic-aware trip planning. First, we construct a dynamic point-of-interest network model by extracting relevant information from crowdsourced LBSN and taxi GPS traces. Then, we propose a two-phase approach for personalized trip planning. In the route search phase, TripPlanner works interactively with users to generate candidate routes with specified venues. In the route augmentation phase, TripPlanner applies heuristic algorithms to add user's preferred venues iteratively to the candidate routes, with the objective of maximizing the route score while satisfying both the venue visiting time and total travel time constraints. To validate the efficiency and effectiveness of the proposed approach, extensive empirical studies were performed on two real-world data sets from the city of San Francisco, which contain more than 391 900 passenger delivery trips generated by 536 taxis in a month and 110 214 check-ins left by 15 680 Foursquare users in six months. Lane relative vehicle navigation and control requires accurate lane-relative positioning of the vehicle. This relative position can be computed by comparing the vehicle absolute position with analytic roadway maps, which requires both high-accuracy positioning of the vehicle and high-accuracy lane maps. Carrier Phase Differential GPS (CPDGPS) aided INS or CPDGPS aided encoders is capable of estimating vehicle absolute position (relative to earth center) with centimeter level accuracy; however, to the best of the author's knowledge, the accuracy of lane level maps is currently not sufficient. In this paper, we first consider the structure of lane level maps that are compatible with standard practices of GIS road modeling. Then, various analytic lane definition are discussed. We also present a method of building lane level maps from high-accuracy positioning data along the lane center. The data is segmented according to road intersections. Shape points (vertices) as a function of arclength are located based on changes in estimated curvature. For each segment, the parameters are estimated by least-square criteria and can be refined as new datasets become available. This process is shown by an example. A driver's choice of a route to a destination may depend on the route's length and travel time, but a multitude of other, possibly hard-to-formalize aspects, may also factor into the driver's decision. There is evidence that a driver's choice of route is context dependent, e.g., varies across time, and that route choice also varies from driver to driver. In contrast, conventional routing services support little in the way of context dependence, and they deliver the same routes to all drivers. We study how to identify context-aware driving preferences for individual drivers from historical trajectories, and thus how to provide foundations for personalized navigation, but also professional driver education and traffic planning. We provide techniques that are able to capture time-dependent and uncertain properties of dynamic travel costs, such as travel time and fuel consumption, from trajectories, and we provide techniques capable of capturing the driving behaviors of different drivers in terms of multiple dynamic travel costs. Further, we propose techniques that are able to identify a driver's contexts and then to identify driving preferences for each context using historical trajectories from the driver. Empirical studies with a large trajectory data set offer insight into the design properties of the proposed techniques and suggest that they are effective. This paper presents a Cloud-based system computing customized and practically fast driving routes for an end user using (historical and real-time) traffic conditions and driver behavior. In this system, GPS-equipped taxicabs are employed as mobile sensors constantly probing the traffic rhythm of a city and taxi drivers' intelligence in choosing driving directions in the physical world. Meanwhile, a Cloud aggregates and mines the information from these taxis and other sources from the Internet, like Web maps and weather forecast. The Cloud builds a model incorporating day of the week, time of day, weather conditions, and individual driving strategies (both of the taxi drivers and of the end user for whom the route is being computed). Using this model, our system predicts the traffic conditions of a future time (when the computed route is actually driven) and performs a self-adaptive driving direction service for a particular user. This service gradually learns a user's driving behavior from the user's GPS logs and customizes the fastest route for the user with the help of the Cloud. We evaluate our service using a real-world dataset generated by over 33,000 taxis over a period of 3 months in Beijing. As a result, our service accurately estimates the travel time of a route for a user; hence finding the fastest route customized for the user. The OpenStreetMap project is a knowledge collective that provides user-generated street maps. OSM follows the peer production model that created Wikipedia; its aim is to create a set of map data that's free to use, editable, and licensed under new copyright schemes. A considerable number of contributors edit the world map collaboratively using the OSM technical infrastructure, and a core group, estimated at approximately 40 volunteers, dedicate their time to creating and improving OSM's infrastructure, including maintaining the server, writing the core software that handles the transactions with the server, and creating cartographical outputs. There's also a growing community of software developers who develop software tools to make OSM data available for further use across different application domains, software platforms, and hardware devices. The OSM project's hub is the main OSM Web site.