Evaluating defensive driving behaviour based on safe distance between vehicles: A case study using computer vision on UAV videos at urban roundabout

• A case study of lane indiscipline and heterogeneous traffic of densely populated and developing countries. • Methodology to assess defensive driving behaviour using UAV videos and computer vision. • Weighted Safe Distance W S D and Weighted Reward for Acceleration W R A - Novel surrogate safety measures based on stopping distance, blind spots, and driver’s reaction. • Star rating scheme for evaluating defensive driving behaviour. • Study collision risk regarding road infrastructure. While driving, maintaining a sufficient distance helps reduce collision risk. A time gap of two or three seconds on urban roads from a vehicle ahead is advised in defensive driving. The scenario becomes even more challenging in densely populated and developing countries because of limited road infrastructure, lane indiscipline, and heterogeneous traffic. The safe distance between vehicles and the driver’s reaction can be used as surrogate safety measures (SSMs) to evaluate defensive driving behaviour. This paper presents a case study evaluating defensive driving behaviour using the vision-based methodology and UAV video. This paper proposes two novel SSMs based on distance and acceleration and studies defensive driving behaviour, such as “for how long did a vehicle keep driving under another vehicle’s blind spots?” and “how is a vehicle driving (an interaction pattern) when another vehicle ahead is in its stopping distance range?.” Finally, each driver’s star rating depends on their interactions with other vehicles. We observed that around 48 % of the vehicles did not follow defensive driving practices. In our vehicle inter-class interaction analyses, we also found 16.6 % Rear-End, 6.3 % Side-Swipe, and 1.5 % Angled collision risks occurred between car-car, car-car, and 2Wheeler(2W)-car, respectively. Our methodology could help traffic law enforcement agencies and policy-makers elevate road traffic safety by taking counter-measures against the low-star vehicle categories in developing countries. Example videos of star rating are available on https://www.youtube.com/@YagnikBhavsar .

Authors

Institutions

Publication Details

Journal
Multimodal Transportation
Published
2025-04-22
DOI
https://doi.org/10.1016/j.multra.2025.100227
Primary Topic
Autonomous Vehicle Technology and Safety
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evaluating defensive driving behaviour based on safe distance between vehicles: A case study using computer vision on UAV videos at urban roundabout

Mazad Zaveri, Mehul S. Raval, Yagnik M Bhavsar, Shaheriar B Zaveri
Multimodal Transportation
Autonomous Vehicle Technology and Safety
article

Evaluating defensive driving behaviour based on safe distance between vehicles: A case study using computer vision on UAV videos at urban roundabout

Mazad Zaveri, Mehul S. Raval, Yagnik M Bhavsar, Shaheriar B Zaveri
article en

Abstract

• A case study of lane indiscipline and heterogeneous traffic of densely populated and developing countries. • Methodology to assess defensive driving behaviour using UAV videos and computer vision. • Weighted Safe Distance W S D and Weighted Reward for Acceleration W R A - Novel surrogate safety measures based on stopping distance, blind spots, and driver’s reaction. • Star rating scheme for evaluating defensive driving behaviour. • Study collision risk regarding road infrastructure. While driving, maintaining a sufficient distance helps reduce collision risk. A time gap of two or three seconds on urban roads from a vehicle ahead is advised in defensive driving. The scenario becomes even more challenging in densely populated and developing countries because of limited road infrastructure, lane indiscipline, and heterogeneous traffic. The safe distance between vehicles and the driver’s reaction can be used as surrogate safety measures (SSMs) to evaluate defensive driving behaviour. This paper presents a case study evaluating defensive driving behaviour using the vision-based methodology and UAV video. This paper proposes two novel SSMs based on distance and acceleration and studies defensive driving behaviour, such as “for how long did a vehicle keep driving under another vehicle’s blind spots?” and “how is a vehicle driving (an interaction pattern) when another vehicle ahead is in its stopping distance range?.” Finally, each driver’s star rating depends on their interactions with other vehicles. We observed that around 48 % of the vehicles did not follow defensive driving practices. In our vehicle inter-class interaction analyses, we also found 16.6 % Rear-End, 6.3 % Side-Swipe, and 1.5 % Angled collision risks occurred between car-car, car-car, and 2Wheeler(2W)-car, respectively. Our methodology could help traffic law enforcement agencies and policy-makers elevate road traffic safety by taking counter-measures against the low-star vehicle categories in developing countries. Example videos of star rating are available on https://www.youtube.com/@YagnikBhavsar .

Multimodal TransportationVol. 4(3)
Ahmedabad University (IN)
Sustainable cities and communities
Openalex Percentile: Top 92%
Autonomous Vehicle Technology and Safety
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.