Autonomous vehicles (AVs) play an important role in transforming our transportation systems and relieving traffic congestion. To guarantee their safety, AVs must be sufficiently tested before they are deployed to public roads. Existing testing often focuses on AVs’ collision avoidance on a given route. There is little work on the systematic testing for AVs’ route planning and tracking on a map. In this paper, we propose CROUTE, a novel testing method based on a new AV testing criterion called route coverage. First, the map is modeled as a labeled Petri net, where roads, junctions, and traffic signs are modeled as places, transitions, and labels, respectively. Second, based on the Petri net, we define junctions’ topology features and route features for junction classification. The topology feature describes the topology of roads forming the junction, and the route feature identifies the actions that a vehicle can take to follow a route. They can characterize route types on a map. Hence, route coverage measures how many route types are covered. We then propose a systematic method that aims to cover all route types for a well-designed AV system with a small number of test cases. We implement and evaluate CROUTE on Baidu Apollo running with the LGSVL simulator. We carry out testing on the map from a section of San Francisco and find six different types of issues in Apollo. The experiment results show the validity of route coverage and the efficiency of CROUTE.

Autonomous Driving Systems (ADSs), which replace humans to drive vehicles, are complex software systems deployed in autonomous vehicles (AVs). Since the execution of ADSs highly relies on maps, it is essential to perform global map-based testing for ADSs to guarantee their correctness and AVs’ safety in different situations. Existing methods focus more on specific scenarios rather than global testing throughout the map. Testing on a global map is challenging since the complex lane connections in a map can generate enormous scenarios. In this work, we propose ATLAS, an approach to ADSs’ collision avoidance testing using map topology-based scenario classification. The core insight of ATLAS is to generate diverse testing scenarios by classifying junction lanes according to their topology-based interaction patterns. First, ATLAS divides the junction lanes into different classes such that an ADS can execute similar collision avoidance maneuvers on the lanes in the same class. Second, for each class, ATLAS selects one junction lane to construct the testing scenario and generate test cases using a genetic algorithm. Finally, we implement and evaluate ATLAS on Baidu Apollo with the LGSVL simulator on the San Francisco map. Results show that ATLAS exposes nine types of real issues in Apollo 6.0 and reduces the number of junction lanes for testing by 98%.

For thermal inversion, observations of TIR are usually used to generate land surface temperature (LST) and atmosphere thermal and moisture profiles. Observations of VNIR are regarded as with little relationship with thermal parameters, as a result, few people use VNIR in thermal inversion. Zhao first used VNIR in thermal inversion and proved that VNIR data is helpful in thermal inversion. However, for land surface researchers, land surface temperature (LST) plays an important role in many applications. A physical algorithm based on quasi-Newton iteration is used on the retrieval of LST. We combined SAS (separation of land surface and atmospheric contribution) algorithm and the physical algorithm, which retrieved LST and atmosphere parameters based on pixel together. In this way, a new algorithm with both microcosmos (pixel) and macroscopy (image) considered is proposed in the paper. The retrieved result showed that the precision of LST retrieval is much higher than the result with original observations

Image filter is to remove the noise of images and try to protect the useful information as much as possible. Edge preserving & noise smoothing is to smooth the polluted image without influencing the detail spatial texture and its spatial structure much. We introduced SAS (separating the contribution of land surface and atmosphere) algorithm zhao proposed in remote sensing inversion area to image filtering area. The test result shows that the new algorithm reveals more powerful capability on protecting spatial texture protection than other algorithms.