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# Current state of the simulation | ||
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**Summary:** The current state of the simulation is assessed by doing three runs of 20 mins (real world time), where all mistakes or anomalies are written down. | ||
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- [Goal](#goal) | ||
- [Methodology](#methodology) | ||
- [Observed Errors Grouped by Domains](#observed-errors-grouped-by-domains) | ||
- [Infrastructure](#infrastructure) | ||
- [Testing and Validation](#testing-and-validation) | ||
- [Perception](#perception) | ||
- [Localization and Mapping](#localization-and-mapping) | ||
- [Decision-Making](#decision-making) | ||
- [Path Planning](#path-planning) | ||
- [Control](#control) | ||
- [Raw notes](#raw-notes) | ||
- [Run 1](#run-1) | ||
- [Run 2](#run-2) | ||
- [Run 3](#run-3) | ||
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## Goal | ||
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In order to understand the current state of the agent, it is crucial to assess the status quo and note the challenges it is faced with. | ||
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## Methodology | ||
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This assessment was done by three leaderboard runs in the CARLA simulator with the handover-state for PAF24. While doing so, all mistakes made by the agent have been noted, as well as possible anomalies occurring during the inspection. | ||
After the review, the mistakes have been grouped by the roles defined in the project in order to make it easier to address the challenges in the respective domains. **Note:** Some mistakes overlap and communication is key when tackling these issues. | ||
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## Observed Errors Grouped by Domains | ||
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### Infrastructure | ||
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These issues relate to foundational aspects of the simulation environment and underlying software stability: | ||
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- **Simulator Performance Degradation:** | ||
- Simulation slows down over time (from .33 to .29 rate), potentially impacting reaction times and sensor data processing. | ||
- **Vehicle Despawning:** | ||
- Random despawning of cars and potential timeout for stuck vehicles may interfere with the agent’s perception and response. | ||
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--- | ||
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### Testing and Validation | ||
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These errors highlight the gaps in the testing and validation process, particularly areas that may need further testing to ensure proper functioning in the real environment: | ||
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- **Consistency in Object Detection:** | ||
- Image segmentation flickering (e.g., police car with indicators), suggesting inadequate validation for dynamic objects with flashing lights. | ||
- **Vision Node Stability:** | ||
- Vision node appears to freeze occasionally, indicating possible untested scenarios or bugs in the perception pipeline. | ||
- **Unrealistic Emergency Braking and Recovery Testing:** | ||
- Unstable lane holding and recovery, resulting in inappropriate emergency braking maneuvers, suggests insufficient validation in complex recovery scenarios. | ||
- **Misclassification of Tree Trunks:** | ||
- Trees being detected as cars, indicating the need for validation of object detection in diverse environmental conditions. | ||
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--- | ||
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### Perception | ||
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Errors within perception involve how the agent senses and understands its surroundings: | ||
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- **Object Misclassification and Collision:** | ||
- Tree trunks mistakenly detected as cars. | ||
- Crashes into bikers and parked cars, suggesting perception failures in identifying and avoiding static and moving obstacles. | ||
- **Segmentation and Detection Instability:** | ||
- Vision node freezing. | ||
- Flickering segmentation for objects like police cars with indicators. | ||
- **Lane Detection and Holding Errors:** | ||
- Difficulty in stable lane holding, leading to unexpected lane deviations and emergency braking. | ||
- Misinterpretation of open car doors, causing lane intrusions without sufficient clearance. | ||
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--- | ||
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### Localization and Mapping | ||
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Issues with localization and mapping involve understanding and positioning within the environment: | ||
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- **Positioning Errors in Turns:** | ||
- Turns are too wide, leading the agent onto the walkway, indicating potential localization issues in tight maneuvers. | ||
- **Lane Holding and Position Drift:** | ||
- Unstable lane holding with constant left and right drifting suggests potential mapping or localization inaccuracies. | ||
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--- | ||
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### Decision-Making | ||
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Errors in decision-making relate to the agent's ability to make appropriate choices in response to various scenarios: | ||
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- **Right of Way Violations:** | ||
- Fails to yield to oncoming traffic when turning left and when merging into traffic. | ||
- Ignores open car doors when passing parked cars, causing dangerous close passes. | ||
- **Erroneous Stopping and Acceleration:** | ||
- Stops unnecessarily at green lights and struggles to resume smoothly after stopping. | ||
- Abrupt stopping and starting at green lights, potentially due to aggressive speed control. | ||
- **Repeated Mistakes in Overtaking and Lane Changes:** | ||
- Treats temporary parked cars as regular vehicles to overtake without checking oncoming traffic, leading to unsafe lane changes. | ||
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--- | ||
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### Path Planning | ||
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Path planning issues include errors in determining the correct and safest path: | ||
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- **Incorrect Overtaking Paths:** | ||
- Attempts to overtake trees and temporary parked cars without considering oncoming traffic, showing flaws in path generation. | ||
- **Wide Turning Paths:** | ||
- Takes overly wide turns that lead to walkway intrusions. | ||
- **Aggressive Lane Changes:** | ||
- Lane change planning is overly aggressive, causing the vehicle to abruptly veer, triggering emergency stops to avoid collisions. | ||
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--- | ||
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### Control | ||
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Control-related issues concern the vehicle’s execution of planned actions, like maintaining speed and stability: | ||
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- **Abrupt and Aggressive Speed Control:** | ||
- Speed controller is too aggressive when accelerating from green lights, leading to abrupt stopping and starting. | ||
- **Instability in Lane Holding:** | ||
- Inconsistent lane holding, particularly after getting unstuck, results in unexpected deviations onto walkways. | ||
- **Inconsistent Recovery Behavior:** | ||
- Repeatedly gets stuck in various situations (e.g., speed limit signs or temporary parked cars) and fails to recover smoothly, indicating control issues in re-engaging the driving path. | ||
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--- | ||
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## Raw notes | ||
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Here are the raw notes in case misunderstandings have been made when grouping the mistakes | ||
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### Run 1 | ||
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- Scared to get out of parking spot | ||
- lane not held causing problems when avoiding open car door | ||
- stopping for no apparent reason | ||
- does not keep lane (going left and right) | ||
- driving into still standing car at red light | ||
- impatient when waiting for light to turn green (after the crash, going back and forth) | ||
- abrupt stopping and going when light turns green without reason → speed controller too aggressive? | ||
- Problems to keep lane is causing emergency(?) brake maneuvers | ||
- vision node seems to be frozen ? | ||
- Detects bikers, crashes into them nonetheless | ||
- lane change very aggressive causing emergency stop in order to not go into oncoming traffic | ||
- gets stuck as a result | ||
- simulator despawns cars randomly | ||
- left turn does not give way to oncoming traffic when seeing them | ||
- does the turn too wide, gets onto walkway | ||
- simulation gets slower as time progresses, started at .33 rate, now at .29 | ||
- gets stuck in front of speed limit sign after doing turn too wide | ||
- gets unstuck, lane holding too aggressive goes onto walkway again (integrator windup while being stuck?) | ||
- gets stuck again (→ unstuck behavior bad) | ||
- when getting unstuck, merges onto street without giving way to traffic on the road | ||
- drives into oncoming traffic, traffic on the same lane overtakes on the right side and does not stop | ||
- really stuck now | ||
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### Run 2 | ||
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- merges without giving way to traffic | ||
- does not respect open car door | ||
- crashes into car in front when going after stop at red light | ||
- stops at green light | ||
- crashes into bikers | ||
- kid runs onto street, agent crashes into oncoming traffic, gets stuck | ||
- nudges away from the car it crashed into | ||
- is now free but does not move | ||
- crashes again | ||
- police car with indicators on standing on the side is crashed into | ||
- image segmentation for police car seems to be flickering | ||
- tree trunk has bounding box (are trees detected as cars?) | ||
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### Run 3 | ||
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- does not give way when exiting a parking spot | ||
- LIDAR detects floor | ||
- trajectory for overtaking is wrong / no overtake needed | ||
- stops without reason | ||
- tries to "overtake" tree (detects tree as car) | ||
- playback ration temperature dependent likely | ||
- after emergency brake stops too long | ||
- left turn doesn't give way to oncoming traffic | ||
- recovery leads to oncoming traffic (left turn situation maybe doesn't recognize street?) 9 min | ||
- temporary parked car with indicators on counts as normal overtake (does not check oncoming traffic) | ||
- temporary parked car with indicators is the crux | ||
- Despawn time of cars ? Cars despawn when stuck → over time limit ? | ||
- Trajectory correctly generated, just too deep in the mistakes |