RiskBench: A Scenario-based Risk Identification Benchmark

Chi-Hsi Kung¹, Chieh-Chi Yang¹, Pang-Yuan Pao¹, Shu-Wei Lu¹, Pin-Lun Chen¹, Hsin-Cheng Lu², Yi-Ting Chen¹

¹National Yang Ming Chiao Tung University, ²National Taiwan University

2024 IEEE International Conference on Robotics and Automation (ICRA)

Abstract

This work focuses on risk identification, i.e., the ability to identify and analyze risks from dynamic traffic participants and unexpected events. While significant advances have been made in the community, the current evaluation of risk identification algorithms uses independent datasets, leading to difficulty in direct comparison and hindering collective progress toward safety performance enhancement.

To address this limitation, we introduce RiskBench, the largest scenario-based benchmark for risk identification. Our benchmark is created using a scenario-based approach, which is widely accepted in the automotive industry. We design a scenario taxonomy and augmentation pipeline to enable the systematic collection of ground truth risk under different scenarios. We assess the ability of nine algorithms to (1) detect and locate risks, (2) anticipate risks, and (3) facilitate decision-making. We conduct extensive experiments and summarize future research on risk identification. Our aim is to encourage collaborative endeavors in achieving a society with zero collisions.

Interaction Types

Our interaction type includes: Interactive: yielding to dynamic risk, Collision: crashing scenario, Obstacle: interacting with static elements, and Non-interactive: normal driving, aiming to cover the different definitions of risks discussed in the community.

Interactive

Collision

Obstacle

Non-interactive

Data Collection

A scenario taxonomy and data augmentation pipeline are developed to collect a range of diverse scenarios in a procedural manner. The taxonomy includes various attributes such as road topology, scenario types, ego vehicle behavior, and traffic participants’ behavior. From this taxonomy, if a scenario script is set, two human subjects can act accordingly. To form the final scenario dataset, we augment the collected scenario by changing attributes, including time of day, weather conditions, and traffic density.

Experiment Setups

Risk Identification Baselines

The baselines take a sequence of historical data as input and output a risk score for each road user (e.g., vehicle or pedestrian) or an unexpected event (e.g., collision or construction zone). We consider a road user or an unexpected event risk if the score exceeds a predefined threshold. We implement 10 risk identification algorithms and categorize them into the following four types.

Rule-based: Random (randomly pick an object) and Range (pick any objects within a predefined distance).
Trajectory Prediction and Collision Checking: Kalman filter [1], SocialGAN [2], MANTRA [3], and QCNet [4].
Collision Anticipation: DSA [5] and RRL [6].
Behavior Prediction-based: BP [7] and BCP [8].

For training details, please refer to our github.

Evaluation Metrics

We devise three metrics that evaluate the ability of a risk identification algorithm to (1) identify locations of risks, (2) anticipate risks, and (3) facilitate decision-making.

Risk Localization: We utilize precision and recall for risk localization evaluation and the False Alarm Rate for Non-interactive scenarios (no risky dynamic elements or events), i.e., a positive prediction is a false alarm.

Risk Anticipation: We propose Progressive Increasing Cost (PIC), an anticipation and localization-aware metric. Our insight is that we penalize a false identification more when we are closer to the risk than far from the risk. Given a scenario with T frames, we calculate the F-1 score for each frame t and define the metric as $\textrm{PIC} = -\sum_{t=1}^{n} e^{-(T-t)}\mathtt{log}(F1_{t})$ .

Planning Awareness: We propose Influenced Ratio (IR) to evaluate if a risk identification algorithm can facilitate decision-making. Assume we access an ideal planner that can interact with traffic participants and unexpected events. IR is defined as IR = |D_orig − D_post|/D_orig, where the parameter D_orig denotes the closest distance while interacting between the ego vehicle and the true risk in a scenario using the full observation. The parameter D_post denotes the closest distance while interacting between the ego vehicle and the true risk with the masked observation.
An ideal planner should be able to yield to the crossing vehicle as shown in (a). If a risk identification model successfully identifies the vehicle as the risk, we hide all the other objects. The planner should plan a slow-down path, as shown in (b). In contrast, if the model identifies the wrong object as risk (e.g., the green one), the planner will plan a path to (nearly) collide with the True Risk, as shown in (c).

Localization and Anticipation Demo

Qualitative Results For Risk Identification

Fine-grained Scenario-based Analysis

Planning-aware Demo

Temporal Consistency

We study the temporal consistency of models’ predictions, as shown in the following table. To evaluate temporal consistency, we determine if a risk is predicted accurately and consistently within a specified time frame, leading up to the critical/collision point.

	1s	2s	3s
QCNet [4]	50.2%	26.9%	18.5%
DSA [5]	14.0%	4.6%	3.8%
RRL [6]	19.0%	8.5%	4.7%
BP [7]	4.2%	2.4%	1.9%
BCP [8]	6.9%	3.9%	3.4%

Dataset Details

Sensor Suite

We collect camera/depth/instance segmentation images from all front view sensors. Our perspective-view camera has a 120-degree field of view.

In addition, we also collect object bboxs and precise lane mark which can render BEV images ( max pixel per merter: 10 ).

Alongside the camera data, we also collect LiDAR, GNSS, IMU. Our data is captured at a frame rate of 20 Hz.

Scenario Attributes

The following table summarizes all possible values of each taxonomy attribute. The maximum number of Basic Scenario that the proposed taxonomy can describe is 547 which is calculated without considering Map and Area ID.

Attribute	Value
Map	Town01, Town02, Town03, Town05, Town06, Town07, Town10HD, A0, A1, A6, B3, B7, B8
Road topology	forward, left turn, right turn, u-turn, lane change left, right lane change
Interaction type	interactive, collision, obstacle, non-interactive
Interacting agent type	car, truck, bicyclist, motorcyclist, pedestrian
Interacting agent's behavior	forward, left turn, right turn, u-turn, lane change left, right lane change, crosswalking, jaywalking, go into roundabout, go around roundabout, exit roundabout
Obstacle type	traffic cone, barrier, warning, illegal parking vehicle
Traffic violation	Running red light, ignoring stop sign, driving on sidewalk, jay-walker
Ego's reaction	right-deviation, left-deviation

Labeled Area for Data Collection

Maps from CARLA simulator. The notations ’i’, ’t1’, ''t2', 't3', ’s’, and ’r’ indicate 4-way intersection, T-intersection-A, T-intersection-B, T-intersection-C, straight, and roundabout.

Additional Maps from Real World. To overcome the limited number of roundabouts in CARLA, we incorporate real-world maps from CAROM-Air [9] and reconstruct them in the CARLA simulator. Specifically, we select A0, A1, A6, B3, B7, B8 from CAROM-Air.

Dataset Splits

	Interactive	Collision	Obstacle	Non-interactive
Training	925	1044	850	1023
Validation	348	283	258	496
Testing	521	375	322	471

Citation

@article{kung2023riskbench,
  title={RiskBench: A Scenario-based Benchmark for Risk Identification},
  author={Kung, Chi-Hsi and Yang, Chieh-Chi and Pao, Pang-Yuan and Lu, Shu-Wei and Chen, Pin-Lun and Lu, Hsin-Cheng and Chen, Yi-Ting},
  journal={arXiv preprint arXiv:2312.01659},
  year={2023}
}

If you have any questions, please contact Yi-Ting Chen.

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[9] Lu et al., "CAROM Air -- Vehicle Localization and Traffic Scene Reconstruction from Aerial Videos". ICRA 2023.