Developing a Risk Recognition System Based on a Large Language Model for Autonomous Driving ^†

Abstract

Autonomous driving systems have the potential to reduce traffic accidents dramatically; however, conventional modules often struggle to accurately detect risks in complex environments. This study presents a novel risk recognition system that integrates the reasoning capabilities of a large language model (LLM), specifically GPT-4, with traffic engineering domain knowledge. By incorporating surrogate safety measures such as time-to-collision (TTC) alongside traditional sensor and image data, our approach enhances the vehicle’s ability to interpret and react to potentially dangerous situations. Utilizing the realistic 3D simulation environment of CARLA, the proposed framework extracts comprehensive data—including object identification, distance, TTC, and vehicle dynamics—and reformulates this information into natural language inputs for GPT-4. The LLM then provides risk assessments with detailed justifications, guiding the autonomous vehicle to execute appropriate control commands. The experimental results demonstrate that the LLM-based module outperforms conventional systems by maintaining safer distances, achieving more stable TTC values, and delivering smoother acceleration control during dangerous scenarios. This fusion of LLM reasoning with traffic engineering principles not only improves the reliability of risk recognition but also lays a robust foundation for future real-time applications and dataset development in autonomous driving safety.

1. Introduction

Recent NHTSA reports indicate that 94% of traffic accidents stem from human error [1], prompting significant interest in the use of autonomous driving for accident prevention [2]. However, current systems still struggle to interpret the complex interactions among small vehicles, emergency vehicles, and pedestrians [3]. To address these shortcomings, recent research has begun leveraging large language models (LLMs) such as GPT-4 [4], which exhibit emergent reasoning capabilities comparable to human judgment [5]. However, many studies overlook transportation domain knowledge—particularly surrogate safety measures (SSMs)—and rely on unrealistic 2D simulations.

This study enhances risk recognition and system reliability by integrating GPT-4 with the time-to-collision (TTC) metric in a realistic 3D simulation environment, CARLA 0.9.14. Our approach combines advanced LLM reasoning with critical transportation engineering insights, offering a promising direction for more robust and context-aware autonomous driving systems.

2. Methods

This study aims to detect potentially dangerous situations that are apparent to humans but challenging for conventional models. We implemented a dangerous scenario in CARLA, an open-source 3D simulation platform. Specifically, we selected the viaduct area of CARLA, where shadows and parked vehicles create visibility obstacles, as shown in Figure 1.

In our scenario, a child unexpectedly crosses in front of a parked vehicle at 5 m/s, requiring the autonomous vehicle to detect this risk from approximately 30 m away and respond appropriately. Our framework in Figure 2 operates the ego-vehicle in autonomous mode within CARLA and extracts key data: a front RGB image, four pieces of surrounding object information (object ID, type, Euclidean distance, and time-to-collision), and five driving parameters (speed, acceleration, throttle, steering, and brake). This information is reformatted into natural language and input into GPT-4, which evaluates the traffic situation and determines if a dangerous condition exists—responding with “YES” or “NO”, along with its reasoning. Based on GPT-4’s judgment, vehicle control commands are issued, and CARLA simulates the resulting traffic safety outcomes.

3. Results

As a result of the provided information and the request that GPT determine the risk situation, it was recommended that brakes be applied to the autonomous vehicle, as shown in

Table 1. Results of GPT-4-based risk assessment.

GPT-4 Questions

GPT-4 Answers

The attached image is from the front camera of an autonomous vehicle, namely ego-vehicle. The following is the result of detecting the surrounding objects of ego-vehicle. The unit of Distance is “m” and the unit of TTC (Time to Collision) is “s”. [Detected Objects] Object ID: 27, Type: walker.pedestrian.0049, Distance: 19.19, TTC: 2.0083130971714924 The following parameters represent the driving status of the Ego-vehicle. [Ego-vehicle Information] Speed: 9.69 m/s, Acceleration: (−2.40, −0.43, 0.00) m/s2 Throttle: 0.00, Steering: −0.02, Brake: 0.15 Do not just consider numbers like the position of objects, but pay attention to the behavior of the vehicle or pedestrian in the image. In particular, be aware of potentially dangerous situations, such as jaywalking pedestrians. Even if the likelihood of a collision is low right now, you never know what might happen. Be prepared for the risk of a potential accident. Even a pedestrian on a pedestrian road can distinguish potential hazards from his gestures. Again, consider the context of the image as a whole. Given the three pieces of information above, does this ego-vehicle need to maintain or start the brakes? Answer YES or NO. When answering, answer yes or no first, and then take a paragraph to explain your reasoning. Be aware of visibility restrictions caused by parked cars. Pedestrians, including children, may appear suddenly. Detect the head, torso, feet, etc. of an obscured child within a limited field of view. If you see children, they might be playing and run into the driveway

Yes. Given the information, the ego-vehicle needs to maintain or, if necessary, apply more braking. The provided object detection shows that there is a pedestrian(walker.pedestrian.0049) at a distance of 19.19 m and with a Time to Collision (TTC) of approximately 2 s. Considering the speed of the ego-vehicle is 9.69 m/s, and the pedestrian is in close proximity to the vehicle’s path, active braking is crucial to avoid a potential collision. Furthermore, the ego-vehicle’s current brake value is 0.15, and its acceleration is negative, indicating that it is already decelerating, which should be continued or increased if needed, to assure the safety of the pedestrian and comply with traffic safety regulations. The image provided corroborates the need for caution. The pedestrian is visible on the sidewalk, and there are also parked cars that could obstruct the view of other potential pedestrians, especially children, who may enter the street unpredictably. Situational awareness is key in such environments, and the autonomous vehicle’s systems must ac-count for obscured areas and the possibility of sudden pedestrian movements. The vehicle’s de-cision algorithms should prioritize pedestrian safety and prepare to stop if necessary, considering the vehicle’s speed and the pedestrian’s proximity to the potential path of the vehicle.

. To evaluate performance, we extracted the distance to the child pedestrian, TTC, and vehicle acceleration at 0.1 s intervals. Figure 3 indicates that the LLM-based module significantly improves traffic safety compared to the default module. With the default module, the distance was reduced to 6.2 m at 1.7 s after control, whereas the LLM-based module maintained a distance of 11.8 m at 1.3 s, about 5.6 m greater. Moreover, the LLM-based module provided a more stable TTC, achieving 0.38 s at 1.2 s versus 0.04 s at 1.7 s with the default module. Additionally, the default module exhibited erratic acceleration patterns, failing to recognize dangerous situations. These findings highlight the benefits of integrating LLM with domain knowledge using TTC as a critical safety metric to enhance autonomous driving performance. While CARLA-based simulations validate the approach, future work must incorporate real-time capabilities. The proposed framework serves as a foundation for building a learning dataset for further advancements in autonomous perception technology.