jkk_dataset_03

This dataset contains the raw data of naturalistic driving, utilized for Human-Like Behavior studies of Automated Vehicles (HLB4AV).

`jkk_dataset_03.zip`¶

Download from here or get it with wget:

wget https://laesze-my.sharepoint.com/personal/igneczi_gergo_ferenc_hallgato_sze_hu/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Figneczi%5Fgergo%5Fferenc%5Fhallgato%5Fsze%5Fhu%2FDocuments%2FDataset%2Fjkk%5Fdataset%5F03&ga=1 -O leaf-2022-03-18-gyor.bag

The data contains information of 17 drivers, who were selected to have relevant driving experience. Dr001-Dr003 are professional drivers who have extra driving certificate. The following table shows the details of participants:

Driver ID	Type	Age	Driving Experience	Driving Frequency	Milage per year
001	N-P	31	10+	3	4
002	N-P	32	10+	3	4
003	N-P	28	10+	3	5
004	N-P	31	10+	4	5
005	N-P	46	10+	4	4
006	N-P	25	6-10	2	2
007	N-P	29	10+	3	3
008	N-P	28	3-6	2	3
009	N-P	28	1-3	1	2
010	N-P	43	10+	4	4
011	N-P	31	10+	3	3
012	N-P	44	10+	3	3
013	N-P	52	10+	4	4
014	N-P	36	10+	4	5
015	N-P	32	10+	3	4
021	N-P	51	10+	4	5
023	N-P	39	10+	2	3

Explanation to notations:
- P: Professional, N-P: Non-Professional
- Driving Frequency: 1: few times a year, 2: few times a month, 3: few times a week, 4: every day.
- Driving Milage per year: 1: 0-1000km, 2: 1000-3000km, 3: 5000-10000km, 4: 10000-25000km, 5: more than 25000km.

The dataset uses the following coordinate system of the vehicle:

Even though the data is recorded considering only two dimensional movement, the Z axis is displayed to give the right explanation of the yaw rate signal. Always, positive direction of a rotational quantity means CCW direction.

Reference Platform¶

Data was recorded using two different vehicle platforms.

The reference platform is a dedicated test vehicle, equipped with multiple environment sensors, also with direct access to the CAN network of the vehicle. The type of the vehicle is a Volkswagen Golf VII Variant, with a 1.4 TSI engine and 7-shift automatic gearbox.

There are various sensor devices in this vehicle:
- Genesys ADMA Gen3 (DGPS), source: ADMA 3.0 Technical Documentation, Document revision: 1.9, Date: 02/2019. Device includes the sensing of vehicle kinematic states.
- Bosch Second Generation Multi-Purpose Camera (MPC2.5), including lane edge detection.
- Bosch Fourth Generation Radar sensor, including object detection in ego lane and the two neighbouring lanes.
- CAN data, steering torque and steering angle values.

Reference Platform was used for measurements with Driver 1, 2 and 3 (professional drivers).

Test Platform¶

The test platform is a vehicle which is used when nonprofessional drivers are measured. The test vehicle is a Skoda Octavia MK3, with automatic gearbox. The vehicle systems were not modified, and no driver-assistance function was activated during testing. This vehicle was only equipped with the Genesys ADMA Gen3 (DGPS) device. The lane position information was reconstructed from the static lane map and the localization of the vehicle.

Offline Calculations¶

Static Lane Map¶

Based on the reference measurements, the lane map of the test route was created. For this, the lane position provided by the video camera was used. The lane position accuracy has +/- 2cm, while the localization has accuracy of +/- 1 cm.

The camera provides information of the position of the lane edges at each sample time. The following information are provided:
- lane position ($d$)
- lane orientation ($\Theta$)
- lane curvature ($\kappa$).
These data are then interpolated to a resolution of 5 cm travel distance through the route, using MATLAB function spline. Also, the lane edge position are transformed to the global UTM coordinates considering the pose of the vehicle. In the end, the map data contains high resolution geometry of the lane edges and also the higher level geometrical quantities (orientation and curvature).

Lane Reconstruction¶

For the test platform, the static lane map information is transferred back to the vehicle coordinate frame at each time sample. This replaced the video camera information, therefore the lane position of the test vehicle is available, with an accuracy of +/- 3 cm. Unfortunately, the dynamic information (e.g., other objects in the lane) are not available for the test platform.

Dash-cam data¶

For multiple drivers' data (currently noted by "_withTraffic" tag in the file name) a dash cam video is available. This video is not applicable to use for e.g., computer vision algorithms, but serve more as an informal source of traffic data. However, by manual labelling the following information were added to the data:
1. Oncoming traffic time to pass: when a vehicle appears on the camera image, a timer starts to count down from a certain initial value, until the vehicle passes the test vehicle. Initial value is 2.38 seconds for small vehicles (higher preceeding speed) and 3.38 seconds for trucks (lower preceeding speed). Times were calculated based on the experiments. When there is no oncoming traffic detected, time-to-pass is set to 2.38 seconds, reflecting the fact that a vehicle may turn up in any minute. Therefore, drivers are assumed to be prepared as there would already be oncoming traffic. When a vehicle is followed, the initial value is decreased to the half for both types of vehicles.
2. Oncoming traffic type: 0: no oncoming traffic, 1: small vehicle, 2: truck and 3: convoy.

An example of a convoy, passing the ego vehicle is shown here:

The same scenario on the dash cam video (snippets cut by hand):

Please be noted, that this information is added manually, therefore not suitable for quantitative evaluation, only qualitative!

Summary of Signals¶

Signal Name	Description	Unit	Range	Availability	Source
VelocityX	Longitudinal velocity of the vehicle	m/s	0 - 40	both platforms	ADMA
SteeringTorque	Torque applied by the driver on the steering wheel	Nm	+/-3	reference platforms	CAN
LaneOrientation	Orientation of the mid-lane in the ego frame (positive: CW, negative: CCW)	rad	+/- 0.1	both platforms	Camera
LaneEdgePositionRight	Position of the lane edge of the ego lane on right side (positive: left, negative: right)	m	+/- 2	both platforms	Camera
LaneEdgePositionLeft	Position of the lane edge of the ego lane on left side (positive: left, negative: right)	m	+/- 2	both platforms	Camera
LaneCurvature	Curvature of the lane in the vehicle position	1/m	+/- 0.005	both platforms	Camera
ObjectDistanceFront	Distance of the vehicle in the ego lane (if no vehicle is present, value is zero)	m	0-250 m	reference platforms	Radar
ObjectVelocityFront	Absolute velocity of the vehicle in the ego lane (if no vehicle is present, value is zero)	m/s	0-40	reference platforms	Radar
ObjectAccelerationFront	Absolute acceleration of the vehicle in the ego lane (if no vehicle is present, value is zero)	m/s^2	+/- 10	reference platforms	Radar
YawRate	Yawrate of the vehicle around the Z axis	rad/s	+/- 0.1	both platforms	ADMA
AccelerationX	Longitudinal acceleration of the vehicle	m/s^2	+/- 10	both platforms	ADMA
AccelerationY	Longitudinal acceleration of the vehicle	m/s^2	+/- 10	both platforms	ADMA
RoadWheelAngleFront	Road-wheel-angle of the front wheels	rad	+/- 0.1	reference platforms	CAN
GPS_time	Global GPS time	ms	-	both platforms	ADMA
GPS_status	Global GPS status	enum	1: GPS, 2: RTK float, 4: RTK_course, 8: RTK_Fixed	both platforms	ADMA
LongPos_abs	Global longitudinal position of the vehicle	°	-	both platforms	ADMA
LatPos_abs	Global lateral position of the vehicle	°	-	both platforms	ADMA
Relative_time	Relative time stamp to the beginning of the measurement	s	-	both platforms	ADMA