Automotive

In the automotive domain, the following use cases are defined:

HIGHWAY PILOT

Highly automated driving on single- or multi-lane highways is one of the first applications for passenger cars as well as commercial vehicles. It allows the driver to turn attention away from the driving task for a longer period. The vehicle handles all of the driving-related tasks like overtaking slower vehicles, driving in tunnels or through toll booths. In addition, the system initiates the process of returning responsibility back to the driver. In emergency situations the functions execute defined actions. The use case “Highway Pilot” focuses on:

  • Description of test requirements of vehicle functions for 24/7 usage at any possible environmental condition like bright/dark, hot/cold, dry/wet
  • Semi-virtual validation of sensor performance and algorithms in a real vehicle
  • Seamless validation tool chain from Model-in-the loop (MiL) to Vehicle-in the-loop (ViL) following the ENABLE-S3 architecture
  • Generation of test scenarios for highway pilot
  • Comparability of tests in different test environments (MiL, ViL, Driver-in-the-loop DiL

automotive

The DrivingCube is an automotive test bed (power train test bed or alternatively a chassis dynamometer) extended by physical sensor stimulators. It is used to reproduce plausible, physically correct environmental conditions for the vehicle and therefore for the automated driving function. The test bed includes dynos, vehicle fixation and hardware interfaces for the sensor stimulators. In order to facilitate the development and facilitate the demonstration, there is also a small-scale 1:10 version of the test bed.  The intention of this demonstrator is to integrate the different tools and methods, related to the Highway Pilot testing functions that have been developed during the project. The car is equipped with an automated highway pilot function as well as the required sensors. The considered sensors will be camera, radar, and ultrasonic (just used for the small 1:10 scale version of the driving cube). The environment is simulated in Vires VTD.

INTERSECTION CROSSING

The crossing of a road intersection is difficult and might lead to hazardous situations. In urban areas, almost 50% of traffic accidents with physical injuries happen within intersections and branches. Left turns at intersections are particularly challenging because of the large number of factors, which must be considered. An important factor is the surrounding traffic  especially the vehicles on the oncoming lanes, which should be crossed during a left turn. This use case focuses on:

  • developing processes, tools and experimental trials to validate decision-making functions
  • considering theoretical frameworks, using digital models, and executing a limited number of field trials
  • determining all likely external and internal disturbances autonomous systems might encounter in real traffic conditions
  • demonstrating safe intersection crossing, validation & verification over a defined scenario set

A first UC2 experiment is planned for September 2017. It will focus on optimization of the scenarios and KPIs and will take place on Proving Ground and within a Vehicle in the Loop (ViL) Simulator. In both environments the same ACPS within the same vehicle will drive through the same scenarios over the same intersection. Thereby a scenario based comparison of the validation results between reality and simulation will be possible. This comparison will take place in another experiment in 2018 as soon as further suitable models (for: vehicle, sensors, C2X and environment) are available by Enable-S3 bricks.

The first model available for the UC2 experiment in 2017 is the 3D Environment Model. This Model provides an accurate Digital Map (for the ACPS) as well as 3D Environment of the proving ground intersection (for the simulation).

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UC2 Test Intersection on Proving Ground (Source: Aerial image from City of Brunswick 2014);
Right: Intersection as 3D Environment Model “first Version” (Source: DLR)

CONTEXT-AWARE IN-CAR REASONING SYSTEM

The main objective of this Use Case is the development of the cognitive engine for the in-car reasoning system. The benchmarks for testing the system will be based on data from Dublin, Ireland, comprising traffic volumes, speeds, accidents, intersection cycle time, and major events held in the area, together with data from social media and news feed. Further, data on route preferences and re-routing preferences will allow for the evaluation of recommender systems. Moreover, it is the goal to develop and test signalling strategies for achieving balancing of traffic loads.

We consider the connected car scenario and aim to push the limits of off-line testing as far as possible within this scenario. In our use case, a context-aware in-car reasoning system supports passengers in their travel decisions. For instance, in the context of a motorway ride, assume that a large traffic accident occurs on the planned route. The reasoning system would detect that the accident will affect the arrival time at the destination, alert the driver and provide, upon request, alternative routes specifying the consequences in terms of cost, average arrival time and uncertainty in arrival time. It would also provide assistance to the driver in choosing among the proposed alternatives by asking information about preferences (for instance preferring to take a toll road to ensure a high probability of arriving at a given time). To this end the in-car reasoning system must have a contextual understanding of the vehicle’s surroundings and be able to interact in a meaningful manner with the driver. This interaction can involve advice and discussion of route options up to context aware alerts issued to the driver.

TRAFFIC JAM PILOT WITH V2X COMMUNICATION

This Use Case is intended to allow vehicles to safely drive their passengers through high-density traffic at speeds up to 50-kph without driver input by using a mixture of adaptive cruise control and lane-keep assist systems. In the proposed approach, this system will be enhanced with information coming through V2X communications, eventually allowing the creation of vehicle platoons. The main research topics are based on the development and validation of safety mechanisms to be used by automotive V2X applications and preventing hacking as well as impersonation of “V2X-enabled” systems.
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VALET PARKING

Valet Parking is expected to be the first commercially available automated driving function at SAE Level 4. In this Use Case, methods and tools are explored for developing a safe automated parking system. This includes:

  • the specification of a test system that allows automated scenario generation and execution
  • distributed system under test
    that incorporates in-vehicle and remote functional components in the Parking Area Manager (PAM)
  • functional safety concept concerning the implications of safety regulations and relevant existing standard
  • test coverage analysis
  • perception models of different sensor types