資料集名稱

KITTI

Oxford RobotCar

論文連結 http://robotcar-dataset.robots.ox.ac.uk/images/robotcar_ijrr.pdf
Over the period of May 2014 to December 2015 we traversed a route through central Oxford twice a week on average using the Oxford RobotCar platform, an autonomous Nissan LEAF. This resulted in over 1000km of recorded driving with almost 20 million images collected from 6 cameras mounted to the vehicle, along with LIDAR, GPS and INS ground truth.
資料集採集時走過的路線：

Data was collected in all weather conditions, including heavy rain, night, direct sunlight and snow. Road and building works over the period of a year significantly changed sections of the route from the beginning to the end of data collection.

By frequently traversing the same route over the period of a year we enable research investigating long-term localisation and mapping for autonomous vehicles in real-world, dynamic urban environments.
在不同天氣、光線情況和交通狀況下的資料集中的示例圖：

Cityscape

Comma.ai

Udacity

Udacity為其自動駕駛演算法比賽專門準備的資料集
The dataset includes driving in Mountain View California and neighboring cities during daylight conditions. It contains over 65,000 labels across 9,423 frames collected from a Point Grey research cameras running at full resolution of 1920x1200 at 2hz. The dataset was annotated by CrowdAI using a combination of machine learning and humans.
Labels
Car
Truck
Pedestrian

This dataset is similar to dataset 1 but contains additional fields for occlusion and an additional label for traffic lights. The dataset was annotated entirely by humans using Autti and is slightly larger with 15,000 frames.

Labels
Car
Truck
Pedestrian
Street Lights

BDDV

Berkeley的deepdrive研究組的用於自動駕駛的大規模資料集。包括視訊資料集，影象分割資料集，目標檢測和可行駛區域的資料集。
視訊資料集：
Explore over 400 hours of HD video sequences across many different times in the day, weather conditions, and driving scenarios. Our video sequences also include GPS locations, IMU data, and timestamps.
分割資料集：
Explore over 5000 diverse images with pixel-level and rich instance-level annotations.

GTA5

中文名俠盜獵車手？一款賽車遊戲，現在也被用來訓練自動駕駛的模型。
GTA5中幾乎涵蓋了各種各樣的道路狀況，包括山區、郊區和城市。還有各種各樣的車輛，比如警車、救護車、計程車、貨車等車型。
據說Uber的研究人員在訓練他們的自動駕駛模型用來玩GTA5這款遊戲。

TORCS

This is the official site of TORCS, The Open Racing Car Simulator. TORCS is a highly portable multi platform car racing simulation. It is used as ordinary car racing game, as AI racing game and as research platform. It runs on Linux (all architectures, 32 and 64 bit, little and big endian), FreeBSD, OpenSolaris, MacOSX and Windows (32 and 64 bit). The source code of TORCS is licensed under the GPL (“Open Source”). You find more information about the project in the menu bar on the left. If you need help have a look at the FAQ first, I added a new Researchers section. You can contact us on the torcs-users mailing list (you need to subscribe to use it because of spam).

TORCS features many different cars, tracks, and opponents to race against. You can steer with a joystick or steering wheel, if the device is supported by your platform. It is also possible to drive with the mouse or the keyboard. Graphic features lighting, smoke, skid marks and glowing brake disks. The simulation features a simple damage model, collisions, tire and wheel properties (springs, dampers, stiffness, …), aerodynamics (ground effect, spoilers, …) and much more. The game play allows different types of races from the simple practice session up to the championship. Enjoy racing against your friends in the split screen mode with up to four human players.

TORCS was initially created by Eric Espié and Christophe Guionneau, substantial parts have been added by other contributors (have a look into the “Credits” section for details). The project is currently headed by Bernhard Wymann.The TORCS source code is licensed under the terms of the GNU General Public License (GPL 2), most of the artwork is licensed under the Free Art License, have a look into the packages for details about copyright holders and the licensing.

The next big development goal is an online racing mode.

CARLA

論文連結 https://arxiv.org/pdf/1711.03938.pdf
英特爾&豐田聯合開源城市駕駛模擬器CARLA
CARLA is an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions.

Carcraft

谷歌母公司Alphabet的自動駕駛子公司Waymo開發的一款軟體，用來在諸如加州山景城和得克薩斯州奧斯汀等虛擬重建城市中測試無人駕駛汽車軟體。該公司每天要開1287萬公里的虛擬里程，專注於特別棘手的道路狀況。
在虛擬的奧斯汀、山景城、鳳凰城，以及那些模擬的測試場景裡，有25000輛虛擬的無人車穿梭其中。它們每天總共要行駛800萬英里（約1287萬公里），去年一整年，Waymo的虛擬無人車行駛了25億英里，而實體測試車全年累積的里程，只有300萬英里。

另外，Waymo還在美國加州中央山谷地區的小城默塞德附近建了一個叫做castle的無人駕駛基地，綜合了多種路況，利用多種道具建立了一個小型“城市”。